Virtual reality for movement disorder diagnosis and/or treatment

ABSTRACT

Methods and/or systems for diagnosing, monitoring and/or treating persons at risk for falling and/or other pathological conditions. In an exemplary embodiment of the invention, people are diagnosed before they actually start falling. Optionally, the diagnosis includes trying out and identifying one or more fall triggers using virtual reality tools. Optionally or alternatively, treatment includes training the persons using situations and/or triggers which are determined to be relevant for that person.

RELATED APPLICATIONS

PCT Patent Application No. PCT/IB2012/055453 is related to co-filed PCTPatent Application No. PCT/IB2012/055454, titled: “FREEZING OF GAIT(FOG), DETECTION, PREDICTION AND/OR TREATMENT” the disclosure of whichis incorporated herein by reference. In an exemplary embodiment of theinvention, provocation and/or training programs as described therein arepresented using methods and apparatus as described herein.

This application is a continuation of U.S. patent Ser. No. 14/350,567filed on Apr. 9, 2014, which is a National Phase of PCT PatentApplication No. PCT/IB2012/055453 having International Filing Date ofOct. 9, 2012, which claims the benefit of priority under 35 USC 119(e)of U.S. Provisional Patent Application Nos. 61/545,164 and 61/545,161,both filed on Oct. 9, 2011.

The contents of the above applications are all incorporated by referenceas if fully set forth herein in their entirety.

FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates todiagnosing, monitoring and/or treating persons with a fall risk and/orother pathological conditions.

Falls are a leading cause of morbidity and mortality among older adultsand have a tremendous impact on health care economics, social function,and quality of life.

Gait impairments and falls are ubiquitous among older adults andpatients with common neurological diseases. Approximately 30% ofcommunity-dwelling adults over the age of 65 fall at least once a year.The consequences of these falls may be severe, leading toinstitutionalization, loss of functional independence, disability, fearof falling, depression and social isolation. Attending to this problemis of great importance as the aging population in the world iscontinuously growing, and expected to double by the year 2030, reaching70 million older adults over the age of 65.

In the year 2000 there were approximately 35 million adults aged 65years and older in the US. By the year 2030 the older population mayreach 70 million. According to figures released in 2006 by the UnitedStates Center for Disease Control and Prevention (CDC), about 5.8million (15.9%) persons aged 65 years and older reported falling atleast once during just a three month period, and 1.8 million (31.3%) ofthose who fell sustained an injury that resulted in a doctor visit orrestricted activity for at least one day. In the elderly population,falls are the leading cause for disability and loss of independence. TheCDC recently estimated that 19 billion dollars were spent on non-fatalfall related injuries in the year 2000 alone. Similar relative numbershave been reported in Europe and in Israel. The health care dollarsspent on falls in the west is only expected to rise as the number ofolder adults continues to increase. But even when there is no physicalinjury, a fall often produces fear of falling, social isolation, andself-imposed restrictions in activities of daily living that may furtherincrease fall risk and curtail independence.

Most falls occur during walking and, not surprisingly, gait impairmentshave been associated with an increased risk of falls. Gait abnormalitiesin elderly fallers include reduced gait speed, stride length, andincreased stride symmetry. Fear of falling, a cautious gait, gaitunsteadiness, or inconsistency and dysrhythmicity of stepping have beenrecognized as mediators of fall risk. Another risk factor identified asa cause for falls in the elderly is obstacle crossing abilities.Compared to healthy young adults, older adults walk more slowly duringobstacle crossing, with smaller steps landing dangerously closer to theobstacle with their lead limb. Age-related deficits in vision,proprioception, visual-spatial orientation, and attention can alsonegatively impact postural stability and lower limb kinematics whencrossing obstacles.

During the past two decades, much research on falls has focused ondetermining “intrinsic” and “extrinsic” risk factors.

While there are many motor changes that contribute to fall risk, thesechanges do not always adequately explain the magnitude of thisincidence. There is a growing body of research that specifically linksthe cognitive sub-domains of attention and executive function (EF) togait alterations and fall risk, especially dual task ability. EF andattentional reserves are reduced with ageing. This reduction placesolder adults at a heightened risk of falling when they attempt toperform two or more tasks simultaneously, even if the tasks areotherwise considered to be automatic or demand minimal attention.

Over the past two decades, tremendous advances have been made in theunderstanding of the factors that contribute to falls and manymulti-factorial interventions have been developed¹⁻¹¹. Unfortunately,however, because of limited health care dollars, current clinicalconsensus suggests reserving these interventions for people with a highfall risk^(2, 3). This requires the ability to predict future falls andquantify fall risk. Thus, because of the tremendous impact of falls onfunctional independence, health care economics, and quality of life,much effort has been devoted to the development and evaluation ofoptimal measures of fall risk^(3, 12-21).

Various systems have been proposed to automatically identify falls, sothat an action can be triggered to help alleviate the damage caused bythe fall. However detecting falls upon occurrence can only provide asolution for treatment or alerting help. Few solutions have beenidentified for the detection of individuals at risk of falls before theactual first fall. These tend to include a uni-dimensional medicalassessment of balance and mobility function that is usually done in adoctor's office or a laboratory setting under less than normalconditions.

Interventions designed to reduce the risk of falls have also beendeveloped and tested. More recently, however, specific forms of exercisehave been recommended as elements of fall-prevention programs for olderadults. For example, aerobic-type exercises and exercises that targetbalance, strength and gait are common elements of multifactorial fallprevention interventions. Typically, these exercises report a reductionin fall risk by only about 10% to 20% and are not yet optimal.

A large and rapidly increasing number of randomized controlled trialsinvestigating the effectiveness of fall-preventive options have beenpublished over the last decade. Many preventive intervention programsbased on reported risk factors have been proposed and evaluated. Thesehave included exercise programs to improve strength or balance,education programs, medication optimization, and environmentalmodification. Most exercise programs have focused on training theindividual and attempted to improve and impairment that caused theincreased risk. Earlier reviews suggested that multi-factorialinterventions that combine both motor and cognitive tasks to enhancestability and improve dual tasking abilities in the elderly populationmay be among the most effective, and the American Geriatrics Society andBritish Geriatrics Society recommended this approach as a primarytreatment strategy in their guideline for prevention of falls. To date,however, there is no consensus as to the efficacy, type of intervention,frequency or intensity of the intervention that can be widely used andreadily reproduced for successful prevention of falls.

Additional background art includes the addressing fall risk. Subsensoryvibratory noise provided by insoles containing vibrating actuators wasused for reducing gait variability in a population of elderly recurrentfallers²². Results were modest and with no long term effect. Thevibratory insoles can provide only treatment; once individuals at riskare identified they can be treated with this device in a task specificmanner to address gait variability and stability. Another technologicaldevice used to address fall risk is the Balance master (SMART EquiTest)which provides both an objective assessment of balance control andpostural stability under dynamic test conditions and can also be usedfor training. Evidence has shown some improvement in balance after usingthis approach²³.

REFERENCE LIST

-   (1) Rizzo J A, Baker D I, McAvay G, Tinetti M E. The    cost-effectiveness of a multifactorial targeted prevention program    for falls among community elderly persons. Med Care 1996;    34(9):954-69.-   (2) Ganz D A, Bao Y, Shekelle P G, Rubenstein L Z. Will my patient    fall? JAMA 2007; 297(1):77-86.-   (3) AGS Guidelines. Guideline for the prevention of falls in older    persons. American Geriatrics Society, British Geriatrics Society,    and American Academy of Orthopaedic Surgeons Panel on Falls    Prevention. J Am Geriatr Soc 2001; 49(5):664-72.-   (4) Liu-Ambrose T, Khan K M, Eng J J, Janssen P A, Lord S R, McKay    H A. Resistance and agility training reduce fall risk in women aged    75 to 85 with low bone mass: a 6-month randomized, controlled trial.    J Am Geriatr Soc 2004; 52(5):657-65.-   (5) Lord S R. Aging and falls: causes and prevention. J    Musculoskelet Neuronal Interact 2007; 7(4):347.-   (6) Lord S R, Fitzpatrick R C. Choice stepping reaction time: a    composite measure of falls risk in older people. J Gerontol A Biol    Sci Med Sci 2001; 56(10):M627-M632.-   (7) Lord S R, Menz H B, Sherrington C. Home environment risk factors    for falls in older people and the efficacy of home modifications.    Age Ageing 2006; 35 Suppl 2:ii55-ii59.-   (8) Lord S R, Clark R D, Webster I W. Physiological factors    associated with falls in an elderly population. J Am Geriatr Soc    1991; 39(12):1194-200.-   (9) Menz H B, Lord S R, Fitzpatrick R C. A structural equation model    relating impaired sensorimotor function, fear of falling and gait    patterns in older people. Gait Posture 2007; 25(2):243-9.-   (10) St George R J, Delbaere K, Williams P, Lord S R. Sleep Quality    and Falls in Older People Living in Self- and Assisted-Care    Villages. Gerontology 2008.-   (11) Voukelatos A, Cumming R G, Lord S R, Rissel C. A randomized,    controlled trial of tai chi for the prevention of falls: the Central    Sydney tai chi trial. J Am Geriatr Soc 2007; 55(8):1185-91.

(12) Herman T, Inbar-Borovsky N, Brozgol M, Giladi N, Hausdorff J M. TheDynamic Gait Index in healthy older adults: The role of stair climbing,fear of falling and gender. Gait Posture 2008.

-   (13) Podsiadlo D, Richardson S. The timed “Up & Go”: a test of basic    functional mobility for frail elderly persons. J Am Geriatr Soc    1991; 39(2):142-8.-   (14) Tinetti M E. Performance-oriented assessment of mobility    problems in elderly patients. J Am Geriatr Soc 1986; 34(2):119-26.-   (15) Verghese J, Buschke H, Viola L, Katz M, Hall C, Kuslansky G,    Lipton R. Validity of divided attention tasks in predicting falls in    older individuals: a preliminary study. J Am Geriatr Soc 2002;    50(9):1572-6.-   (16) Visser J E, Carpenter M G, van der K H, Bloem B R. The clinical    utility of posturography. Clin Neurophysiol 2008.-   (17) Thurman D J, Stevens J A, Rao J K. Practice parameter:    Assessing patients in a neurology practice for risk of falls (an    evidence-based review): report of the Quality Standards Subcommittee    of the American Academy of Neurology. Neurology 2008; 70(6):473-9.-   (18) Berg K O, Wood-Dauphinee S L, Williams J I, Maki B. Measuring    balance in the elderly: validation of an instrument. Can J Public    Health 1992; 83 Suppl 2:S7-11.-   (19) Delbaere K, Close J C, Menz H B, Cumming R G, Cameron I D,    Sambrook P N, March L M, Lord S R. Development and validation of    fall risk screening tools for use in residential aged care    facilities. Med J Aust 2008; 189(4):193-6.

(20) Narayanan M R, Lord S R, Budge M M, Celler B G, Lovell N H. Fallsmanagement: detection and prevention, using a waist-mounted triaxialaccelerometer. Conf Proc IEEE Eng Med Biol Soc 2007; 2007:4037-40.

-   (21) Whitney J C, Lord S R, Close J C. Streamlining assessment and    intervention in a falls clinic using the Timed Up and Go Test and    Physiological Profile Assessments. Age Ageing 2005; 34(6):567-71.-   (22) Galica A M, Kang H G, Priplata A A, D'Andrea S E, Starobinets O    V, Sorond F A, Cupples L A, Lipsitz L A. Subsensory vibrations to    the feet reduce gait variability in elderly fallers. Gait Posture    2009; 30(3):383-7.-   (23) Kammerlind A S, Hakansson J K, Skogsberg M C. Effects of    balance training in elderly people with nonperipheral vertigo and    unsteadiness. Clin Rehabil 2001; 15(5):463-70.-   (24) Exp Neurol. 2009 February; 215(2):334-41. Knee trembling during    freezing of gait represents multiple anticipatory postural    adjustments. Jacobs J V, Nutt J G, Carlson-Kuhta P, Stephens M,    Horak F B.

SUMMARY OF THE INVENTION

A broad aspect of some embodiments of the invention relates to provokingfalls and/or other pathological conditions using triggers and/orsituations in order to diagnose, monitor and/or treat persons at risk offalling and/or other pathological conditions.

An aspect of some embodiments of the invention relates to a method offall risk assessment, comprising:

presenting a subject with a plurality of provocations selected to inducea fall or near fall; and

generating a risk assessment based on response of the subject to theprovocations.

In an exemplary embodiment of the invention, subject is selected forscreening, before any falls occur. Optionally or alternatively, thesubject is selected for monitoring. Optionally or alternatively, theassessment is part of a training program. Optionally or alternatively,presenting comprises presenting using virtual reality (VR). Optionallyor alternatively, presenting comprises presenting situations of varyingcomplexity. Optionally or alternatively, presenting comprises presentingtriggers of varying difficulty, during an ongoing scene presentation.Optionally or alternatively, presenting comprises presentingprovocations both expected to induce falls or near falls and thoseexpected not to induce falls or near falls. Optionally or alternatively,presenting comprises presenting dual motor and cognitive tasks.

In an exemplary embodiment of the invention, presenting comprisespersonalizing the presentation to the subject performance and/or subjectclinical history. Optionally or alternatively, the method comprisesidentifying one or more parameters of a situation and/or a trigger whichinduce falls or near falls in the patient. Optionally, the methodcomprises setting up a training program responsive to the identifying.Optionally or alternatively, the method comprises modifying saidprovocations in response to said identifying.

In an exemplary embodiment of the invention, said provocations visuallysimulate daily activities of the subject.

An aspect of some embodiments of the invention relates to a method oftreating a subject at risk of falling, comprising:

presenting a subject with a plurality of provocations selected to inducea fall or near fall for a plurality of sessions. Optionally, saidpresenting comprises presenting according to a training plan. Optionallyor alternatively, said presenting comprises presenting according to aprogress of the subject. Optionally or alternatively, the methodcomprises presenting the subject with one or both of a knowledge ofperformance and a knowledge of results, to assist in his training.

An aspect of some embodiments of the invention relates to apparatus forfall and/or near induction and/or near-induction, comprising:

(a) a display;

(b) a controller configured to present one or more provocationscalculated to induce a fall or near fall on the display. Optionally,said controller is configured to select said provocations personalizedfor a particular subject. Optionally or alternatively, said display is avirtual reality (VR) display.

In an exemplary embodiment of the invention, the apparatus comprises aplurality of wearable modules. Optionally, a module is wireless andincludes one or both of a sensor and an actuator.

In an exemplary embodiment of the invention, the apparatus comprises atreadmill controlled by said controller.

An aspect of some embodiments of the invention relates to method ofassessment of gait pathologies, comprising:

presenting a subject with a plurality of provocations selected to inducethe occurrence of a pathological behavior; and

generating a risk assessment based on response of the subject to theprovocations. In an exemplary embodiment of the invention, saidpresenting is under conditions where the subject is not likely to hurthimself by said occurrence.

There is provided in accordance with an exemplary embodiment of theinvention a method of fall risk assessment, comprising:

presenting a subject with a plurality of provocations selected to inducea fall or near fall; and

generating a risk assessment based on response of the subject to theprovocations. Optionally, said generating is based only on near-falls.

In an exemplary embodiment of the invention, said generating is based onat least one fall.

In an exemplary embodiment of the invention, the subject is selected forscreening, before any falls occur.

In an exemplary embodiment of the invention, the subject is selected formonitoring.

In an exemplary embodiment of the invention, the assessment is part of atraining program.

In an exemplary embodiment of the invention, presenting comprisespresenting using virtual reality (VR).

In an exemplary embodiment of the invention, presenting comprisespresenting situations of varying complexity.

In an exemplary embodiment of the invention, presenting comprisespresenting triggers of varying difficulty, during an ongoing scenepresentation.

In an exemplary embodiment of the invention, presenting comprisespresenting provocations both expected to induce falls or near falls andthose expected not to induce falls or near falls.

In an exemplary embodiment of the invention, presenting comprisespresenting dual motor and cognitive tasks.

In an exemplary embodiment of the invention, presenting comprisespersonalizing the presentation to the subject performance and/or subjectclinical history.

In an exemplary embodiment of the invention, the method comprisesidentifying one or more parameters of a situation and/or a trigger whichinduce falls or near falls in the patient. Optionally, the methodcomprises setting up a training program responsive to the identifying.Optionally or alternatively, the method comprises modifying saidprovocations in response to said identifying.

In an exemplary embodiment of the invention, said provocations visuallysimulate daily activities of the subject.

In an exemplary embodiment of the invention, the method comprisesdetecting a near fall based on a change in the pattern of data from oneor more movement sensors, with the support of one or more additionalphysiologic sensors.

In an exemplary embodiment of the invention, the method comprisesdetecting a fall or near fall in a person having fewer than 1 falls ornear falls in 10,000 steps in daily life.

In an exemplary embodiment of the invention, increasing a rate of fallsor near falls over average daily activities by a factor of at least 10.

In an exemplary embodiment of the invention, increasing a rate of fallsor near falls over average daily activities by a factor of at least 100.

There is provided in accordance with an exemplary embodiment of theinvention a method of treating a subject at risk of falling, comprising:

presenting a subject with a plurality of provocations selected to inducea fall or near fall for a plurality of sessions. Optionally, saidpresenting comprises presenting according to a training plan.

In an exemplary embodiment of the invention, said presenting comprisespresenting according to a progress of the subject.

In an exemplary embodiment of the invention, the method comprisespresenting the subject with one or both of a knowledge of performanceand a knowledge of results, to assist in his training.

There is provided in accordance with an exemplary embodiment of theinvention apparatus for fall and/or near induction and/ornear-induction, comprising:

(a) a display;

(b) a controller configured to present one or more provocationscalculated to induce a fall or near fall on the display. Optionally,said controller is configured to select said provocations personalizedfor a particular subject. Optionally, said controller is configured toselect said provocations in response to measurement by the system ofsubject activity. Optionally or alternatively, said controller isconfigured to modify a parameter of a selected provocation in responseto measurement by the system of subject activity. Optionally, saidmodification comprises the controller selecting a provocation whichmatches a weakness of the subject, detected by analysis of saidmeasurement.

In an exemplary embodiment of the invention, said display is a virtualreality (VR) display.

In an exemplary embodiment of the invention, the apparatus comprises aplurality of wearable modules. Optionally, a module is wireless andincludes one or both of a sensor and an actuator.

In an exemplary embodiment of the invention, the apparatus comprises atreadmill controlled by said controller.

In an exemplary embodiment of the invention, said controller isconfigured to detect a fall and a near fall. Optionally, said detectionis based, at least in part, on a relationship between the power innormal gait frequencies and abnormal gait frequencies, in anacceleration signal. Optionally or alternatively, said detection isbased, at least in part using a machine learning classification method.Optionally or alternatively, said detection is based, at least in part,on one or both of a heart rate and an indication of frontal lobeactivity.

In an exemplary embodiment of the invention, said controller isconfigured to measure at least an indication of brain activity.

In an exemplary embodiment of the invention, said controller isconfigured to calculate a fall risk score.

In an exemplary embodiment of the invention, said controller isconfigured to select said provocations in response to a treatment planfor said subject.

There is provided in accordance with an exemplary embodiment of theinvention a method of assessment of one or more gait pathologies,comprising:

presenting a subject with a plurality of provocations selected to inducethe occurrence of a pathological behavior; and

generating a risk assessment based on response of the subject to theprovocations. Optionally, said presenting is under conditions where thesubject is not likely to hurt himself by said occurrence. Optionally,said assessment is based on change of activity in frontal lobes of thesubject. Optionally, said change in activity is detected using EEG.

In an exemplary embodiment of the invention, an increase in blood flowto a frontal lobe is taken to indicate a gait disorder due to lack orover activity in the frontal lobes.

In an exemplary embodiment of the invention, a decrease in blood flow toa frontal lobe is taken to indicate a gait disorder due to frontal lobedysfunction.

In an exemplary embodiment of the invention, said risk assessment isbased also on a background measurement of the patient.

In an exemplary embodiment of the invention, said provocation includesone or more of a cognitive load, a perceptual load and a motor load.

In an exemplary embodiment of the invention, said presenting comprisespresenting while the patient is on a locomotion device.

There is provided in accordance with an exemplary embodiment of theinvention a method of treating a gait disorder, comprising:

measuring one or more physiological parameters of a subject duringlocomotion;

automatically determining a weakness of said subject with respect togait normality, based on said measuring; and

providing provocation based training to said subject based on saiddetermining. Optionally, said physiological parameter includes an APA.

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

Implementation of the method and/or system of embodiments of theinvention can involve performing or completing selected tasks manually,automatically, or a combination thereof. Moreover, according to actualinstrumentation and equipment of embodiments of the method and/or systemof the invention, several selected tasks could be implemented byhardware, by software or by firmware or by a combination thereof usingan operating system.

For example, hardware for performing selected tasks according toembodiments of the invention could be implemented as a chip or acircuit. As software, selected tasks according to embodiments of theinvention could be implemented as a plurality of software instructionsbeing executed by a computer using any suitable operating system. In anexemplary embodiment of the invention, one or more tasks according toexemplary embodiments of method and/or system as described herein areperformed by a data processor, such as a computing platform forexecuting a plurality of instructions. Optionally, the data processorincludes a volatile memory for storing instructions and/or data and/or anon-volatile storage, for example, a magnetic hard-disk and/or removablemedia, for storing instructions and/or data. Optionally, a networkconnection is provided as well. A display and/or a user input devicesuch as a keyboard or mouse are optionally provided as well.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

Some embodiments of the invention are herein described, by way ofexample only, with reference to the accompanying drawings and images.With specific reference now to the drawings in detail, it is stressedthat the particulars shown are by way of example and for purposes ofillustrative discussion of embodiments of the invention. In this regard,the description taken with the drawings makes apparent to those skilledin the art how embodiments of the invention may be practiced.

In the drawings:

FIG. 1 is a schematic diagram of a VR-based system in accordance withexemplary embodiments of the invention;

FIG. 2 is a schematic block diagram of a VR-based system in accordancewith some exemplary embodiments of the invention;

FIG. 3 is a flowchart showing an exemplary gait and/or fall assessmentmethod in accordance with an exemplary embodiment of the invention;

FIG. 4A is an image of a fall assessment system in accordance with anexemplary embodiment of the invention;

FIG. 4B shows a Shimmer ankle sensor used in accordance with anexemplary embodiment of the invention;

FIG. 4C shows an aerial VR view of a testing scenario in accordance withan exemplary embodiment of the invention;

FIG. 4D shows two examples of obstacles as used in accordance with anexemplary embodiment of the invention;

FIG. 5 shows two examples of challenging scenarios, in accordance withexemplary embodiments of the invention;

FIG. 6 shows a table 1 of fall risk quantization and scoring, inaccordance with exemplary embodiments of the invention;

FIG. 7 shows a table 2 of subject characteristics, in accordance withexemplary embodiments of the invention;

FIG. 8 shows acceleration signals of a subject during an obstacle trial,in accordance with exemplary embodiments of the invention;

FIG. 9 shows a table 3 of measurements of consistency, in accordancewith exemplary embodiments of the invention;

FIG. 10 shows a raw acceleration signal and spectral density of thefrequency band of the gait of a faller compared to a controlparticipant, in accordance with exemplary embodiments of the invention;

FIG. 11 shows an acceleration signal of the gait of a subject during ano-obstacle condition and the signal from a cognitive trial in which amisstep is detected, in accordance with exemplary embodiments of theinvention;

FIG. 12 shows a raw signal from an fNIRS sensor including a misstepevent, in accordance with exemplary embodiments of the invention;

FIG. 13 shows a raw fNIRS signal during a time period when no misstep isdetected, in accordance with exemplary embodiments of the invention;

FIG. 14 shows the signals from three physiological sensors, inaccordance with exemplary embodiments of the invention;

FIG. 15 shows a table 4 of testing parameters of subject 1, inaccordance with exemplary embodiments of the invention;

FIG. 16 shows a table 5 of testing parameters of subject 2, inaccordance with exemplary embodiments of the invention;

FIG. 17 shows a table 6 of testing parameters of subject 3, inaccordance with exemplary embodiments of the invention; and

FIG. 18 shows a table 7 of testing parameters of subject 4, inaccordance with exemplary embodiments of the invention.

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates todiagnosing, monitoring and/or treating persons with a fall risk and/orother pathological conditions.

Overview

Some embodiments of the invention make use of the realization that gaitas well as obstacle negotiation heavily relies on the availability ofample cognitive resources, due to the need for motor planning andvisually dependent gait regulation. There is a growing body of researchthat specifically links the cognitive sub-domains of attention andexecutive function (EF) to gait alterations and fall risk. EF apparentlyplays a critical role in the regulation of gait especially underchallenging conditions where decisions need to be made in real-time suchas walking while avoiding obstacles and walking while simultaneouslyperforming another task, i.e., dual tasking (DT). This may explain whyfalls occur so frequently among older adults, as many older adultssuffer from age-associated decline in cognitive function, even thoughthey have not reached the level of “cognitive impairment”. In fact, ithas been recently shown that EF scores and dual tasking gait performancemay predict future falls during 2-years of follow-up among otherwisehealthy older adults who reported no falls in the year prior to thestudy (Herman et al 2010). The use of EF neuropsychological tests topredict future falls allows us to identify a population at risk.

The present invention, in some embodiments thereof, takes this knowledgeone step forward by using a test setting which presents motor and/orcognitive challenges in a manner which may unmask compensatorystrategies and/or detect risk of falls and/or other gait disorders in awider population base, not only those who have mild or minimal cognitiveimpairments but also in individuals that would not show signs inclinical testing. In some embodiments, the test can detect subtle signsof gait impairment, possibly before they would otherwise be manifest.While some signs of diminished performance in high challenging tasks maybe observed in almost all individuals, in an exemplary embodiment of theinvention, it is the pattern of movement and the cumulative informationregarding the performance on these tasks that is used quantify fall riskand/or risk of other gait impairment. For example, a person may havemany mistakes on an obstacle navigation task but his gait pattern maynot suffer and vice versa suggesting sufficient compensatory strategiesto enable recovery from missteps and therefore a low or no-risk offalls.

Since falls are episodic by nature and are most likely the result of afailure of multiple systems, it has been considered difficult toquantitatively assess the risk of a person and their propensity towardsfalls. Missteps or trips have been identified as “mini-falls” that didnot result in a fall either because the person was able to recover orbecause the loss of balance or trip did not reach enough power and mighthave been considered to imply a higher risk for falls. However,identifying missteps (and falls for that matter) requires relying on theperson's self report, which may not be sufficiently reliable (especiallyamong older adults with problems of memory and recall).

In an exemplary embodiment of the invention, there is provided areliable method that will identify potential “fallers”, possibly priorto the first fall, which usually starts the vicious cycle that isdifficult to escape from. It is expected that early intervention, beforethe first fall, will be much more efficacious and cost effective,however, the challenge, answered by some embodiments of the invention,is to identify persons with an increased fall risk in this relativelyearly stage.

In accordance with exemplary embodiments of the invention, an adaptivesystem is provided which may allow for one or more of accurate diagnosisof the risk for falls, quantifying the severity of fall risk and/orproviding treatment that will be personalized and/or tailored for theperson's needs in order to improve functional ability, lower the risk offalls and/or maintain health. In an exemplary embodiment of theinvention, an all-in-one system is provided which uses virtual realitytechnology to introduce challenges and a tailored “stress test” that mayotherwise cause falls, but in a safe environment. The use of a ‘closedloop’ system enables the unmasking of fall risk that may not benoticeable in normal conditions when compensatory strategies can beused. Once fall risk is detected and quantified, an appropriatetreatment can be delivered.

In an exemplary embodiment of the invention, such technology provides afeasible and usable system for diagnosing and quantifying fall risk, forexample, assess the possibility of using the system to identifyindividuals with risk of falls, using, for example, the algorithmsdescribed herein, but not limited thereto, optionally using one or morephysiological measures (e.g., simple, such as heart rate or processed,such as APAs). In an exemplary embodiment of the invention, there isprovided a method to quantify the risk for falls by combining differentparameters of performance provided by a system as described herein orother systems. As noted below, the system, in accordance with someembodiments of the invention can be utilized for therapeutic purposes,for example, by providing the appropriate exposure to circumstances thatare most likely to lead to falls in a given individual. In an exemplaryembodiment of the invention, the rate (e.g., per step) of falls,missteps and/or other gait abnormalities are increased by the system,optionally in a controlled manner, by a factor of, for example, 5, 10,50, 100, 300, 1000 or greater or intermediate factors.

An aspect of some embodiments of the invention relates to using variousdisplay technologies so as to provoke falls and/or near falls in people,e.g., a “gait” or “fall” stress test. In an exemplary embodiment of theinvention, the provocation is selected to use specific triggers and/orsituations. In an exemplary embodiment of the invention, the triggersare selected to have different intensities so as to estimatesusceptibility to falling. Optionally or alternatively, the triggers areselected to be of different types so as to provide an indication of thetypes of situations where falls are more likely and/or to help identifyparticular individual or sets of deficits in a patient, which predisposethe patient to falling. Optionally, such deficits are then treated, forexample, using training, optionally using a same system design and/ortriggers and/or situations, as used for diagnosis.

In an exemplary embodiment of the invention, the provocations(challenges) are applied/modified (e.g., type, frequency, intensity)using a closed loop with respect to the effect of previous challengesand/or using an open loop, with respect to desired diagnosis.

In an exemplary embodiment of the invention, such a ‘fall risk stresstest’ based on physiological measures is optionally used to quantifysuch risks to assess characteristics of the gait disorder of aparticular individual and/or match up the individual to a stereotype ofa known gait disorder behavior, optionally with an associated suggestedtreatment, prognosis and/or daily living advice.

As also shown below, initial studies using some embodiments of theinvention show the ability of a VR system in accordance with someembodiments of the invention to provoke and/or detect gait changesand/or fall risk under safe environmental conditions. For example, anexemplary system was able to provoke missteps on the treadmill and tosensitively detect these events. Further, an optional a fall risk scorethat may be used to provide care to individuals with a high risk forfalling is quantified based on such detection.

In an exemplary embodiment of the invention, the score is a linear sumof considerations, each weighted, for example, according to patientcharacteristics, for example, based on a library of control subjects.Other forms of score formula may be used as well.

In an exemplary embodiment of the invention, efficacy of treatmentand/or changes (or stability) in patient risk, are monitored by periodic(or other) testing of the patient using the diagnosis system.

The identification of individuals who are at risk of falls is based todate mainly on assessing biomechanical structures and medical problemssuch as balance disorders, weakness, visual deficits and neurological ororthopedic impairments. However these deficits only cover a smallportion of the percent of falls in aging.

It is a particular feature of some embodiments of the invention that“provocation” of difficult situations that may cause falls in theelderly is provided and/or used for detecting a threshold that beyond ita person may fall, optionally specifically targeting the cognitive-motorinteractions that are critical to fall risk.

It is a particular feature of some embodiments of the invention that thedisplay technologies used provide a virtual reality (VR) to the patient.This allows, for example, for various situations to be more easily triedout on a patient. Optionally or alternatively, triggers are presentedwhile the patient is in a staged setting (or a virtual reality setting),to assess the effect of the triggers. Optionally or alternatively, thisallows a user to practice at home and/or using relative low costsystems.

In an exemplary embodiment of the invention, the virtual reality displayincludes a head set and/or goggles which show an image and/or an overlayimage to the patient. Optionally, head tracking and/or position trackingis used to adjust the image so it appears realistic (e.g., except forwhen such misalignment is being tested as a trigger). In anotherexample, the “virtual reality” is provided using fixed screens, forexample, facing the patient and/or multiple screens to provide an imagein a greater portion of the patient's viewing field, optionallyincluding peripheral vision as well.

Optionally or alternatively, the virtual reality display includes ahead-mounted sound system. Alternatively, sound, if any, is provided byspeakers located in a room.

In an exemplary embodiment of the invention, assessment of a patient isa multi-factorial risk assessment of falls, optionally includingneurological assessment etc.

Optionally, the virtual reality testing uses the previous assessments asan input to decide which triggers, situations and/or intensity levels(or other trigger parameters) to use.

In an exemplary embodiment of the invention, after assessment, anintervention program, optionally personalized, is selected, optionallyincluding VR training, and/or other interventions, such as cognitivetraining and/or strength training. In an exemplary embodiment of theinvention, the VR training is selected to match the specific weaknessesof the patient and/or include triggers and/or situations which allow notonly for training but also for ongoing monitoring of changes insusceptibility to falling (e.g., including triggers that are assumed tooweak to cause falls in that patient, so as to see that patient did notregress; similarly, for “difficult” triggers, to see if susceptibilitywent down).

It is a particular feature of some embodiments of the invention, that anintensive multi-sensory cueing is used that could affect the impairmentfeatures of gait while also addressing cognitive domains in dual taskingconditions. In an exemplary embodiment of the invention, the systemsand/or methods described herein can be used to specifically target motorand cognitive dual tasking with a VR system for diagnosing and treatmentof elderly fallers.

In an exemplary embodiment of the invention, risk assessment can includeusing, observational (or machine-measured) gait analysis or methodsknown in the art for using characterizing fall risk. Though it is notedthat, in general, using only such methods (e.g., with a VR system) maybe insufficient to quantify what occurs as a subject carries out routineactivities of daily living, and self-report will be relied upon. Infact, despite its subjective nature and the known problems about recall,self-report is the standard for quantifying at-home fall frequency. Itis also noted that unidimensional assessment often does not reflectperformance of daily living where complex, everyday challenges may causethe person to fall.

It is a particular feature of some embodiments of the invention thatdiagnosis monitoring and/or treatment are carried out emulating and/orsimulating situations which reflect daily and/or out-of-the-labexperiences.

In an exemplary embodiment of the invention, the use of a VR or othercomputer controlled display system allows the use of a single system toassess fall risk and provide the treatment to address this risk.

In an exemplary embodiment of the invention, such an ‘all in one’ systemwill have the ability to create environments similar to those found ineveryday life which challenge older adults and cause them to fall. TheVR will take the clinical assessment from the one-dimensional “safe” andartificial medical exam into a more complex multidimensional andrealistic scenario. The provocation of falls and assessment of theproperties that cause and increase risk for each person will allow for amore individualized, effective and targeted treatment.

In an exemplary embodiment of the invention, the situations and/ortriggers used are matched to a patient. Optionally, such matching isbased on self-reporting of a user. Optionally or alternatively, matchingis by first trying out a set of situations and/or triggers and queryingthe patients to their relevance and/or familiarity, while also measuringeffect on the patient (e.g., via fall and/or near fall detection and/orby monitoring gait and/or effect on a dual task motor-cognitive task.

In an exemplary embodiment of the invention, matching is based on imagesprovided by the patient or others, for example, of sidewalks, a home, anold age home, a park and/or other locations and/or of activities thatthe patient participates in. this can be used to build a visually and/orcognitively similar situation to challenge the patient with.

A particular feature of some embodiments of the invention is the use ofthe method for screening of patients. In an exemplary embodiment of theinvention, a screening session is between 30 minutes and one hour wherea patient is challenged with various test situations and triggers. Inone example, the subject walks on the treadmill and is presented withdifferent walking scenarios (e.g., duration of 4 minutes each, forexample between 1 and 10 minutes) which challenge and manipulatedifferent tasks (motor, cognitive, dual task). For example, the personis first asked to walk 4 minutes on the treadmill in his comfortablegait speed to assess his baseline ability and/or normal stride lengthand/or symmetry of walking. Then the person will walk while observingthe VR simulation in which obstacles will be presented. This task can bechallenging with vertical obstacles of 30 cm and horizontal obstacles of1 meter. Such a task may require the participant to negotiate theobstacles without contacting them. The frequency of appearance of theobstacles can be, for example, gradual and random e.g., they will firstappear every 5 strides (6 meters) for 30 seconds, then advance to 3steps (3 meters) and then to 8 m. The system can measure the performanceas a function of the difficulty level. If the person is able to navigatewell at a certain level, then the difficulty level may be increase andthe lowest challenging level may be waived. Optionally, in total, 5trials of 4 minutes each are used. Each trial focuses on a differentaspect relating to fall risk (motor, environmental, cognitive etc). Thetrial in which the most problems have been identified may be repeatedtwice or more (e.g., with different levels of difficulty to more closelyassess the underlying mechanism of falls in this person. Depending onthe results, a patient may be sent for more complete evaluation and/orgiven advice.

In some embodiments, a testing may be provided at home, for example,using a computer display and a home treadmill.

An aspect of some embodiments of the invention relates to testing and/ortreating patients by provoking the patients with situations and/ortriggers designed and/or selected to cause falls, near falls,degradation of gait and/or other cognitive and/or motor and/orfunctional effects.

In an exemplary embodiment of the invention, a system for such a useincludes a display and a controller programmed to provide, using thedisplay, one or more situations and/or triggers. In an exemplaryembodiment of the invention, the controller, for example, a computer, isprogrammed with situations and/or triggers matching to a diagnostic,monitoring and/or treatment plan of the patient. Optionally oralternatively, standardized tests are stored thereon as well. Optionallyor alternatively, standardized scenarios are stored thereon.

In an exemplary embodiment of the invention, the display is a VR display(e.g., a screen, wall projection or goggles). Optionally oralternatively, the display is a standing display. Optionally oralternatively, the triggers are audio triggers. In some embodiments, thesituation is provided, at least in part by a room and its decoration.Optionally, the system (e.g., possibly other than the display) are notcoupled to the patient, so the patient is unencumbered by wires. Forexample, the display and/or any sensors may be wireless. Optionally oralternatively, the controller is also worn by the patient.

In an exemplary embodiment of the invention, the system includes atreadmill (or other platform) for the patient to walk on. In otherembodiments, the patient walks on a floor.

In an exemplary embodiment of the invention, the system can be used tosimultaneously present a situation and/or triggers and also provide adual cognitive task. Optionally, the system includes inputs from theuser (e.g., voice, buttons, touch screen) and/or measurements (e.g., ofgait, falls and/or near falls, for example, using pressure sensorsand/or accelerometers and/or video cameras and/or position sensors) toassess an effect of the situation and/or trigger.

In an exemplary embodiment of the invention, the system is configuredfor remote operation and/or processing of collected data, for example,allowing a system to be placed in an old age home, but operated and/ormonitored by remote.

An aspect of some embodiments of the invention relates to determining afall or near fall or gait problem by activity in the frontal lobes, forexample, based on changes of blood flow thereto. In an exemplaryembodiment of the invention, when such flow is reduced, it is assumedthat the cause of the problem is motor and blood is being diverted tomotor areas. If such flow is increased, it is assumed that the cause ofthe problem is cognitive and blood is diverted to the frontal lobes, forexample, to improve attention or executive function. In some cases, itis assumed that such changes in blood flow indicate the method beingused by the patient to solve the problem. Optionally, training of thepatient includes teaching other methods (e.g., motor rather thancognitive or vice versa) and such feedback is used to assess if patientis learning the more useful response. Some embodiments of the inventionmay be used as an assessment tool to observe the effects of implicitmotor learning and the transfer of this learning to functional abilityand/or may also be used as a neurofeedback approach by which the FNIRS(Functional Near Infra Red Spectroscopy) device is worn on the foreheadof the person while he is training with the VR system. The continuedmeasure of blood flow to the frontal lobe could be coupled to a feedbackmechanism that will provide information on correct or incorrect motorstrategies.

In an exemplary embodiment of the invention, a fall or near fall couldbe detected based on the increase of blood flow, optionally inconjunction with other physiological measures, such as heart rate andacceleration of a limb. In some embodiments, frontal blood flowassessment is for clinical use only whereas ECG and/or galvanic skinconductance (used as an indication of psychological or physiologicalarousal and a measure of the sympathetic nervous system) could bemeasured continuously also during daily life with the ambulatory device.

An aspect of some embodiments of the invention relates to treating agait disorder and/or reducing a risk of falling by training withfall-causing provocations and, optionally, implicitly teachingstrategies of movement that will be efficacious in negotiating theseprovocations. In an exemplary embodiment of the invention, such trainingis used in conjunction with teaching of alternative strategies,cognitive and/or motor and/or mechanical to use in situations where fallrisk is increased and/or if a patient receives an indication that fallrisk is increasing. Such an indication is optionally provided by a worndevice that provides biofeedback when the gait pattern is not correct oris identified as at a risk for falls. This training also allows forimplicit motor learning opportunities due to the feedback provided onknowledge of performance. This feedback may be personalized for theperson's needs and can optionally allow for a graded progression indifficulty level. In some embodiments, a worn or implanted device isused without VR, for example, including a sensor and a stimulator, forexample, being implemented in a smartphone.

An aspect of some embodiments of the invention relates to screening ofpatients for fall risk. A potential benefit of some embodiments of theinvention is that within 20 minutes (e.g., 5 trials of 4 minute walkseach), one can not only assess and diagnose fall risk, but alsounderstand the nature of the individuals' problems and/or possiblyprescribe and/or tailor the most appropriate personalized care that willaddress the person's needs. As an example, an older adult could beevaluated by the system in a clinic as a result his physician canrecommend physical therapy to address issues of foot clearance thatincreases this person's risk for falls. After receiving therapy, theclient will come back to the clinic for another evaluation by the systemto assess the efficacy of the intervention and the current risk forfalls. If there was improvement, the clinician can recommend life stylemodifications such as continued physical activity etc. If there has beenno change or even a deterioration then the clinician can prescribe anintensive intervention via the system described herein. Somecomplimentary parts of some embodiments of the invention enable the useof its components as a screening tool, evaluation and assessment tooland finally as a training device. Optionally, screening is repeatedperiodically, for example, on a tri-monthly, yearly or bi-yearly basisand/or in response to functional changes and/or neurological events inthe subject.

In an exemplary embodiment of the invention, partial screening isprovided by worn accelerometers or other motion sensors which mayidentify a problematic gait. Optionally, such a worn device can providewarning (e.g., pre-fall or near fall) and/or generate an alarm orcommunicate with a center to call a subject in for testing, if a changein risk is detected. Optionally, in-depth diagnosis and/or evaluationare provided using a VR system, for example, at home or at a clinic.Optionally or alternatively, a worn device will also include a cuingsystem to remind a subject to correct his gait and/or enter a defensivemode.

In an exemplary usage, Using the information provided by the system,after a baseline assessment, the system can automatically (or a human,manually) create individually-tailored training programs to train themotor system of the subject to adapt for strategies that distance themfrom the physiological circumstances that lead to falls. This mayempower the provision of quality care, individualized to the person'sneeds. For example, if the evaluation highlights that a subject hasmainly problems with gait asymmetry, the focus of the treatment will beon motor learning that will result in modifying the gait pattern tobecome more coordinated. If the system concludes that the person's riskof falls is high because of cognitive problems and impaired dividedattention then the system can recommend to use the VR simulation toprovide training that is rich with cognitive stimulus tasks such asvisual spatial processing, attention, planning and executive function.In another example, if the person has difficulties in step clearance andobstacle negotiation then the training will focus on provided differentobstacles and teaching the participant strategies of movement. Resultsshown below suggest that training with such a system can improve gait,dual tasking and/or cognitive abilities, and/or lower the risk of falls.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not necessarily limited in itsapplication to the details of construction and the arrangement of thecomponents and/or methods set forth in the following description and/orillustrated in the drawings and/or the Examples. The invention iscapable of other embodiments or of being practiced or carried out invarious ways.

Exemplary Adaptive System and Methods

In an exemplary embodiment of the invention, a “smart” adaptive systemis provided, which allows for the provocation of falls in order toassess the risk of an individual and/or optionally uses this detectioncapability to provide an individualized treatment paradigm, optionallyspecifically designed to address each person's needs, which may lowerthe risk of falls and/or maintain health to the extent possible.

In an exemplary embodiment of the invention, such an ‘all in one’ systemwill comprise of a treadmill and a virtual reality (VR) simulation.

FIG. 1 is a schematic showing of such a system 100, in accordance withan exemplary embodiment of the invention.

A subject (patient) 102 will walk on a treadmill 104 while immersing ina VR environment 108 (or possibly a non-immersive display screen). In anexemplary embodiment of the invention, the subjects will wear a safetyharness 110 to prevent injury but one that does not interfere with theirmobility. Optionally, wearable sensors (or camera readable markers orother types of fiduciary marks) 106 will be attached to specific bodysegments on the person and allow for a closed loop system to detect themovement and reaction to the VR scene and/or to serve as direct input tothe VR scene. The use of a closed loop system (e.g.,acquisition-processing-actuation and again acquisition . . . )optionally reduces the need for continuous assistance by clinicians. Inan exemplary embodiment of the invention, the cameras (e.g., or others,such as “Kinect”-type motion, gesture and posture capture cameras) areused to also collect posture information. Optionally, gait abnormalitiesare detected and/or predicted based on such posture information and/oron a combination of posture information and acceleration data or otherphysiological measurements. Optionally, training uses postureinformation as a feedback to indicate if a patient has learned thedesired coping and/or avoiding strategies.

As shown, a user can be challenged, for example, with cognitive tasksand/or motor tasks. Examples of cognitive challenges include, but arenot limited to, dual tasking, planning and/or scanning. Examples ofmotor challenges include, but are not limited to, instructions to orobstacles that require variations in gait speed step height and/or steplength. Exemplary scenarios shown include obstacle negotiation, regularwalking, endurance walking and/or training.

In an exemplary embodiment of the invention, the system architectureincludes smart wearable nodes (e.g., sensors 106) consisting of bodysensors (e.g. accelerometers, gyroscopes, heart rate monitors, brainmonitors), actuators (e.g., audio and/or vibrotactile), and/ormicrocontrollers (e.g. 8 to 32 bit), that will act as the interface ofthe system. Optionally, wireless communication modules (e.g. Bluetooth(BT) or Zigbee or longer range, such as WiFi or cellular protocols).Built in power supplies (e.g., batteries) may be provided. Optionally,the units serve as the gateway to the VR simulation. These can enable amulti-functional personalized system with multi-modal feedback andsensing.

In an exemplary embodiment of the invention, data from the sensors willbe transferred (via wireless communication) to the computer simulationand projected to the subject. Optionally, the subject will be shown,instead of or in addition to a first person display, a display on whichtheir movement and performance can be seen through a virtual avatarrepresenting them.

In an exemplary embodiment of the invention, VR simulation includes agraphical design which engages the patients and yet is not perceptuallyover loading or too complex to hinder understanding of the targetedpractice. In an exemplary embodiment of the invention, the design of thesystem takes into consideration the target population and the potentialvisual and perceptual changes occurring with age such as diminisheddepth perception, impaired peripheral vision and a decrease in colordefinition. The VR simulation is projected on a large screen and isdesigned to be viewed in 2D. The decision is based on a pilot study thatwas performed using 3D stereoscopic view, which could be used in analternative embodiment of the invention. Elderly adults reported thatthe immersive environment caused dizziness and was to overwhelmingovertime. The 2D application increases the sense of presence but withoutthe potential hazards of cyber sickness. The VR simulation may bematched to the patient's abilities. Optionally, more realistic (thoughoptionally less tasking) situations are used in patients with morelimited ability. In an exemplary embodiment of the invention, the VRsimulation will encompass one or more of obstacle negotiation andcognitive tasks that include tasks of executive function such asdecision making, memory, planning, response selection, responseinhibition, divided attention and sustained attention. Optionally, oneor more tasks are provided in addition to portrayal of a scene, forexample, a mathematical or a listening task.

In an exemplary embodiment of the invention, the system will providescenario's that often induce falls in the elderly in everyday life suchas negotiating obstacles while avoiding distracters and attending to atemporal constraint (e.g., similar to trying to catch a bus). If theperson is able to successfully attend to the task without falling, thesystem will automatically provide a more challenging task. If a fall isprovoked a different scenario will be provided to address otherdifficulties that can cause falls in order to fully assess the person'srisk. These could include walking under visually obstructed conditions,crossing narrow pathways, stressful situations, scenario's that requirequick decision making and so forth.

In an exemplary embodiment of the invention, the tasks and/or scenariosare personalized. Specific tasks could include for example a simulationof a kitchen, in which the person is required to reach forward and grabingredients for a cake from the cupboard. The simulation embodies amotor task, requiring balance, functional reach and center of massdisplacement as well as cognitive tasks that relate to scanning thecupboard and remembering the list of ingredients needed. Another examplecould be the use of a simulation of a board walk on which differenttypes of obstacles are placed. Here the motor task is intuitive andrequires stepping, single limb support, balance, clearance of the footover the obstacle, and the cognitive tasks include planning of theactions required (when to lift the foot to pass over the obstacle),perception and attention. Subsets of the tasks, in which fewer sub-tasksare required, may also be used. Selection of tasks depends, for example,on the person's needs and the specific weakness or impairments toprovide the most appropriate personalized treatment. For example for aperson who has low clearance and is likely to trip and fall because heis unable to lift his feet up high, an obstacle course may beprescribed.

FIG. 2 is a schematic showing of a system 200, in accordance with anexemplary embodiment of the invention. As shown, system 200 has threeconceptual parts, acquisition 204, processing 206 and feedback 208. Someparts of the system, such a display or a processing system may belocated remotely from the other parts. Optionally, reports on a patienttest and/or changes therein may be automatically and/or electronicallysent to a caregiver and/or a monitoring professional, such as aphysician. In an alternative, a remote view may connect to the systemand initiate data collection, for example, to enable a tele-medicineapproach in which a clinician can view the person's performance in realtime.

At acquisition 204, a display 108 is used for one or more of creatingchallenges, creating a cognitive load, engage and/or sustain a subjectand/or provide feedback to the subject. In an exemplary embodiment ofthe invention, sensors and/or a camera 202 are used to collectinformation about patient 102.

At processing 206, a processor, for example, a PC or an embeddedwearable processor provide one or more of generating visualizations,on-line signal processing, fall detection and/or adaptations, such asauto-calibration, adaptation and personalization features. In anexemplary embodiment of the invention, data collected from the sensorsis run through a detection algorithm that identifies high frequencies inthe gait pattern suggesting an over powered walking pattern (which iscommon during missteps and falls). Optionally, information provided bythe VR (e.g., one or more of the number of obstacles successfullypassed, which obstacles, distance of the forefoot from the obstacle whenfirst passing it, the distance of the heel of the advancing foot fromthe obstacle upon initial contact with the ground; i.e., heel stroke) isused to adapt the obstacles continuously and/or change the difficultlevel based on the person's performance system to provide a more guidedevaluation and training.

At feedback 208, identification of patient needs 212 is optionallyprovided and optionally used to vary the processing. Optionally, apersonalized treatment protocol 214 is generated.

In an exemplary embodiment of the invention, the system will be adaptiveand have a “learning” paradigm in which if a person encountersdifficulty with one of the scenario's, the system will introduce asimilar simulation but with a higher level of difficulty and/or adifferent task and/or task type (or situation) to tease out theparameters that increase the risk of falls for this particularindividual. Optionally, a set of tasks and their relationship forselection for testing is provided ahead of time. Alternatively, it maybe manually selected. At least in this sense the system may personalizethe risk for each individual. Optionally, using a mathematicalalgorithm, the system will provide a composite score of the person'srisk of falls. This may include for example one or more of: theconditions he is likely to fall in and the most impaired properties(cognitive or motor) that will cause this person to fall. This form ofdetection may provide a useful individualized and/or accurate assessmentof the person's needs and this composite score will allow the clinicianto assess fall risk and then prescribe the most efficient care.

As noted, a worn (e.g., on belt, necklace or wrist) device may be used,for example, to collect data, to generate alerts and/or provide feedbackto the patient and/or others. In one example, such a device includes oneor more movement sensor, such as accelerometers and/or one or morecerebral activity sensors. The device and/or a paired device processescollected data and generates an alert, generates a signal to the patient(e.g., cuing) and/or communicates with a remote server. For example,such a device may provide an indication of high risk if the patient'sgait changes suddenly, starts to change slowly (e.g., as patient getsdrunk) or as a detector when a fall actually occurs.

FIG. 3 is a flowchart of an exemplary such method of fall assessment, inaccordance with an exemplary embodiment of the invention.

At 302, prior fall history is assessed. If none, the method may be usedfor screening, and starts at a lowest level, 306.

If there is prior fall history, a starting point may be manually orautomatically determined, for example, based on that history.

Levels A-E (306-314) may be tried out, for example, in series, in adifferent order and/or interleaved.

If all levels are passed (e.g., relative to some threshold), it may bedetermined that no risk is detected 322 and no treatment needed (or somepreventive treatment may be provided).

At 316, if a subject fails at some level, a risk factor may beidentified or narrowed down based on that failure and/or based onperformance in other tests (e.g., motor and/or cognitive).

At 318, the difficulty may be increased and/or other parameters changed,for example in order to increase accuracy of diagnosis.

At 320, based on the diagnosis, one or more relevant treatments may besuggested. For example if the system concludes that the person's risk offalls is high because of cognitive problems and impaired dividedattention then the system may prescribe training that is rich withcognitive stimulus tasks such as visual spatial processing, attention,planning and executive function. In another example, if the person hasdifficulties in step clearance and obstacle negotiation then thetraining may focus on provided different obstacles and teaching theparticipant strategies of movement. The system could also identify theparticular risk and the recommendations could be provided to a clinicianwho can potentially prescribe alternative treatments such as physicaltherapy exercises, balance training or general conditioning grouptraining. The levels (A-E) optionally refer to different difficultylevels and the involvement of the cognitive and motor constraints. Forexample: level A could include a simulation with low level obstaclesboth horizontal and vertical with no distracters, level B could includehigher level obstacles and the addition of narrow pathways. In level Cpassages will be introduced, these could include doors, bridges andtunnels, level D will include distracters (auditory, visual) and level Ecould include combined dual task activities, higher level obstacles,narrow pathways, passages, and distracters. Different numbers and/orcontents of levels can be provided. Also, an increase in difficulty canbe using a continuous variable (e.g., delay between start and end oftask) optionally being varied based on previous results. Changingbetween levels may be used, for example, to bracket a patient's abilitywith both low scoring levels and high scoring levels.

Optionally, the system (or a different, optionally compatible, system)is used to provide training based on the assessment. In this regard, VRtraining can be a link between motor and cognitive training and/or ameans of significantly enhancing the clinical utility of the “motor”training while performing cognitive tasks that require attention anddecision making.

In an exemplary embodiment of the invention, task specific andappropriate training can be provided using a VR platform for olderadults. This form of training allows for a varied, meaningful and/orpurposeful context that matches the individual's needs and increasespatient empowerment, while, optionally, maintaining interest (e.g.,using interesting visual scenes as background). Optionally oralternatively, treatment can be graded in terms of both physical andcognitive challenges. For example, the system can use the multisensoryfeedback provided by the VR to augment training and/or driveneuroplasticity pathways. Auditory and visual feedback may beautomatically given by the VR simulation in the form of knowledge ofperformance (KP) if errors occurred (e.g., stepping on an obstacle)and/or knowledge of results (KR) e.g., in the form of total time walkedand total number of obstacles safely avoided (similar to a score in agame) and success/failure ratio. In addition, for some avatar designs(if an avatar is used), the person can see the pattern of movement heperforms by looking at the virtual avatar. The avatar can represent thespecific movements of the person and hence for example if the person iswalking with an asymmetrical gait by which the left leg takes a longerstep than the right, this would appear on the screen (and is optionallymarked) and the person will have real-time feedback which will enablehim to correct and regulate his steps. Similarly, if a person is walkingwith short steps or with a wide base of support, this will be projected,and as a result could elicit a behavioral change. Such feedback is knownto assist in generalized learning by providing participants thereference as to how to correct the next attempt by self-assessment andproblem solving. This feedback is thought to allow for the developmentof new motor programs, transfer, and retention of training effects,creating a behavioral change that has resounding effects on physicalactivity, functional independence and fall risk, but has apparentlyhereinto been unavailable for this type of need. Indeed, as a result ofmotor learning and the behavioral changes, benefits, when implementingsome embodiments of the invention, are expected to persist long afterthe training period, for example, several days, weeks and/or monthsafter a training session and/or a series of training sessions (e.g.,1-18 sessions). In a pilot study that was conducted by the inventors,training effect of 6 weeks of intervention with the system on atreadmill, were sustained for 6 months in 5 healthy elderly women with ahigh risk of falls, even off a treadmill. It is expected that due to theimplicit motor learning and hence plastic brain changes, training effectwill transfer to everyday life activities and will have a sustainedbehavioral and functional effect.

In an exemplary embodiment of the invention, gait assessment is doneusing accelerometers and gyroscopes to assess spatial temporalparameters of gait (e.g., gait velocity and stride time) as well asmeasures of consistency (i.e., gait variability and gait regularity).Optionally, in healthy older individuals, it is expected to observe gaitspeed in a range between 0.8 m/s-1.4 m/s with a mean stride average of1.2 sec and gait variability of approximately 2%. These parameters willchange dramatically in individuals with high risk of falls (e.g., slowergait speed and gait variability of as much as 5% or more). Underchallenging conditions, like those imposed in the VR system, theseparameters will also change (eg. increased variability). The degree towhich they change reflect, in part, the subject's ability to compensateand preserve a normal gait pattern in the face of “extreme conditions”,such as those that occur during complex, everyday walking.

As noted, not only falls need be assessed. For example, one or more ofgait impairments, asymmetry (e.g., as in stroke), orthopedic issues(e.g., even subclinical) dystonia (e.g., episodic gait abnormalities)pre clinical deficits in motor-cognitive performance, ataxia, gaitchanges secondary to psychological deficits (e.g. ADHD, majordepression) and/or others may be detected.

In particular, by challenging the patient with various cognitive, motor,perceptual and/or other loads, rare events can be made more commonand/or more intense.

In particular, diagnosis can include measuring an amount of disorder andquantifying a risk, rather than merely relaying on subjective and binaryassessments by physicians.

In particular, diagnosis is optionally used to pinpoint the underlyingcause (e.g., motor, cognitive, environment, orthopedic, integration,other), so that treatment and/or avoidance can be planned and/ormonitored.

Exemplary usage scenarios One important usage scenario of someembodiments of the invention is using a system as described herein toidentify older adults at risk of falls and provide an early personalizedpreventive intervention that will help them maintain a healthy lifestyle and avoid the complications of falls. Such a system is optionallyset in every (or many) hospital and/or clinic and used as an assessmenttool for clinicians. It can also be used in rehabilitation centers,nursing home facilities and gyms as a training device that will providechallenging, motivating and effective intervention utilizing both motorand cognitive domains, to bring the most effective solution to theproblem.

In an exemplary embodiment of the invention, the system is used toidentify potential “fallers” prior to the first fall, which usuallystarts the vicious cycle of falling, withdrawal and more falling. Sincecurrent treatment methods are not yet optimal, identification of neweffective interventions that reduce the risk of falls is extremelyimportant. Ultimately, a method that would allow predicting who is atrisk of falling and provide an effective treatment approach may help toreduce the costs and burden of falls on society and enhance thefunctional independence of the growing elderly population. In anexemplary embodiment of the invention, a diagnostic, testing and/ortreatment session (they may have different values of the parameters),may be of length, for example, between 3 and 100 minutes, for example,between 10 and 30 minutes. For example, between 1 and 50, for example,between 5 and 20 different provocations may be provided, optionally withsome repetition (e.g., an average of between 1.1 and 4 repetitions perscenario). For example, between 1 and 20, for example, between 3 and 15falls and/or near falls may be collected (or intended to be collected,e.g., by increasing frequency and/or difficulty of challenges and/orvarying type) per session.

In an exemplary embodiment of the invention, a risk assessment includesa score, built of, for example, a weighted combination of the number offalls, number of real falls and/or deficient in cognitive performanceand/or speed of walking, weighted, for example, by the level at whichthey occur. Optionally or alternatively, a table is used whichtranslates performance into a score. Optionally, a multi-dimensionalscore and/or assessment are provided, for example, for different typesof triggers and/or cognitive interference situations. As an example fora person with a history of falls that as per self report of the patientoccurred because of tripping over things, when challenged with anobstacle navigation task, it is expected that the system will detectapproximately 15-20 events with the majority (60-90%) occurring beforeor due to obstacle crossing. This will be complemented by high gaitvariability and step irregularity (˜3% and ˜1.5 prs). All these measurescombined added to high risk of falls score.

Exemplary Invented Cases Mr. Levy is 69 years old, he is retired and isliving in his home with his wife. His wife says he is unstable and isafraid he will fall. Mr. Levy thinks she exaggerates and feels fine. Hegoes to his physician which evaluates him and finds no neurologicalproblem or muscle weakness. In all balance performance based tests, Mr.Levy demonstrates a slight decrease in performance but his score isstill within the normative range for his age. The physician decides tosend him for evaluation in the fall clinic just to be on the safe side.He is referred to the Gait and Neurodynamics Laboratory where he isevaluated by a system using virtual reality. Mr. Levy is started offwith the lowest level of difficulty; obstacles alone. He is able tocomplete the tasks successfully therefore more challenging constraintsare introduced. He is able to negotiate bridges and tunnels and narrowpassages without a problem, but when distracters are introduced (e.g.,level D) he suddenly slows down and his performance is diminished with alow success rate of maneuvering around the obstacles. The systemcomputes his score as moderate and prescribes a training protocol thatincludes high level virtual obstacle navigation under challengingconditions of dual task and distractions. Mr. Levy receives training for8 weeks and declares he feels more confident and able to perform betterduring everyday activities. The scores concur. Both Mr. and Mrs. Levyare happy.

Mrs. Cohen has had PD for 4 years; she recently started suffering frominstability during gait, and even sustained 2 falls in the past year.She reports to her clinician that her first fall occurred while she waswalking in the street talking to a friend and the second one when shewas on a narrow and uneven side walk. The clinician requests that she beevaluated for fall risk and recommends a training activity. She isreferred to the Gait and Neurodynamics Laboratory where she is evaluatedby a system using virtual reality. Because of her prior falls historyshe is put on the system and level B is selected for her evaluation.During the evaluation Mrs Cohen sustains a misstep on the treadmillwhile trying to step over a virtual obstacle. Difficulty level isincreased to include higher and more frequent obstacles. Mrs. Cohenagain fails to negotiate the obstacles safely. Then Mrs. Cohen receivesa new simulation consisting of an urban environment filled withdistracters, here she is hardly able to navigate and successfullycomplete the task. The system defines her risk as high and configures atraining protocol that includes dual tasking activities in differentscenarios. Her training consisted of gait components such as increasingstep length and step clearance while engaging in a navigationalcognitive task that required her to scan the environment for theobstacles, plan the action required, ignore distracters along the way(response inhibition), maintain attention to the task, which task and/orits complexity being optionally based on her performance. Mrs. Cohentrains for 8 weeks on a daily basis, recording on the system, beforeeach training session starts, a self-confidence score about her motorstatus and her medication intake. On-site technical assistance wasprovided when necessary.

Mrs. Jones is a 62 year old woman living in the community. She comes toher physician worried after her best friend sustained a hip fracturesecondary to a fall. She requests a physical check up and anosteoporosis exam. The physician sends her for a bone scan which comesout positive for osteoporosis. Her physical exam however reveals noserious problems in balance, gait or physical fitness. She is referredto the Gait and Neurodynamics Laboratory where she is evaluated by asystem using virtual reality. She is started off with the lowest levelof difficulty; obstacles alone. Mrs. Jones is able to complete the taskssuccessfully therefore more challenging constraints are introduced. Sheis able to negotiate bridges and tunnels and narrow passages as well asthe combined dual tasking challenge (level E). The system computes herrisk score as low and she is sent home with recommendations for safemobility and fall avoidance, but requires no treatment at this time.

Exemplary Variations

The system proposed herein is optionally designed for the elderlypopulation in mind to address the devastating increase in falls and itsconsequences. Many pathological conditions however share similarproblems as the elderly population both in terms of falls risk, but alsoin terms of motor and cognitive deficits.

In an exemplary embodiment of the invention, the systems describedherein are used also or instead for other pathological conditions thanfalls. In such uses, the system is optionally programmed to detect theother pathological condition. Optionally or alternatively, the system isprogrammed with triggers, situations and/or training programs thatprovoke and/or train for the other pathological conditions. Optionally,a training program for multiple conditions is provided, with somesituations adapted for one pathological condition and some for others.

For example, freezing of gait (FOG) may be provoked using, for example,images of narrow passageways, to provoke FOG and/or near FOG conditionsand/or to train a subject for them. Optionally, FOG is detected usingone or more physiological sensors, for example, an accelerometer, whichmay also be used for other disease conditions. Additional details re FOGdetection and/or treatment may be found in a co-filed PCT applicationattorney reference number 54874.

For example the system can detect impairments and/or treat peoplepost-stroke or those affected with neurodegenerative disease. Patientswith neurological conditions often suffer from balance and mobilityimpairments. Issues of symmetry could be addressed as well as difficultyto adjust weight shifting and balance reactions. In addition to themotor problems, neurological pathologies are often accompanied bycognitive deficits such as perceptual impairments, decreased executivefunction, dual tasking ability and frontal inhibition. The integratedsystem could provide an assessment of the impairments and/or deliver themost appropriate type of intervention which could improve theirrehabilitation.

Another example is dystonic gait. This type of gait disorder is verydifficult to assess and treat due to the high variability within andbetween patients. A system like the one described, could possiblyprovoke and uncover the dystonia even in the subclinical phase andpotentially provide the necessary treatment. Extending from this, suchan integrated approach can be of significance for children with CerebralPalsyspinal cord injury and head trauma as well as ataxia both juvenileand traumatic to address their gait pattern, their ability to functionand address challenges in everyday life and provide feedback ofperformance to prescribe appropriate training and treatment. Asexamples, challenges to be used with patients post stroke could relateto improving symmetry and improving weakness on the hemiplegic side.This could be done by providing high level vertical obstacles to thesound limb to encourage more stance on the hemiplegic limb. For patientswith TBI who have difficulties in cognitive function, the system canprovide tasks of planning the path of walking, memorizing differentobjects on the path, and sustaining attention on the task while ambientdistracters appear.

Such a system could also be used to improve performance in the healthypopulation, such as in elite athletes. The system could, for example,assess probability of injury due to deficient gait pattern, subclinicalorthopedic weakness that could cause make the athlete more prone toinjury, such as subclinical asymmetry and over activation (overuse) ofone extremity. Subsequently the system could provide training to enhanceperformance, improve weak spots in a more correct kinematic approach andeven enhance performance on specific tasks such as hurdle running byteaching new improved strategies of performance.

Exemplary Implementation and Experiments

In this section various practical implementations as a system aredescribed, including results from utilizing these implementations fordiagnosing and/or treating people in accordance with some embodiments ofthe invention. It should be noted that the teachings herein are notlimited to the specific system tested.

Exemplary System Architecture

In this exemplary embodiment, the system is designed to integrate bothonline locomotion stimulating techniques and monitoring technologies.The system automatically identifies the walking patterns of theindividual, introduces freezing provoking situations (a kind of‘freezing stress test’) in a controlled environment, quantifies andcharacterizes the freezing episode, and assesses the best repertoire oftreatment suitable for the individual.

FIG. 4A is a picture of such a system. This ‘all in one’ system iscomprised of a treadmill 401, a virtual reality (VR) simulation 402(here shown on a display, rather than, as an alternative, goggles), andaccelerometers 410. The patients walk on treadmill 401 while immersed inthe VR environment 402. Small passive markers are optionally attached tothe patient's shoes or other parts of the patient's body or clothing,optionally using a harness, and act as the interface or gateway to theVR system (e.g., via a camera 408 or other position and/or orientationtracking system). In an alternative embodiment a marker-less trackingsystem is used. Using two optitrack cameras 410, the movements of thefeet are detected and inserted into the VR simulation using an avatar(e.g., as shoes on the screen) that accurately reflected the movement ofthe feet in reaction to the VR scene. Optionally, the patient wears asafety harness. Optionally or alternatively, the patient wear a heartrate monitor 406. Optionally, the subject is wears fNIRS sensors 404(e.g. covered by a head cap) and/or ECG sensors. These cerebral sensorsare optionally used for physiologic monitoring and/or validationpurposes. A controller 412 is optionally used to control and/or readsensors 410 and/or provide input to VR environment 402.

In an exemplary embodiment of the invention, EEG or other means are usedinstead of or in addition to fNIRS to assess changes in cerebralactivity. A potential advantage of using both EEG and fNIRS is that EEGhas better temporal resolution and fNIRS has better spatial resolution.

In an exemplary embodiment of the invention, EEG can be used to measurebrain electrical activity at rest and/or to measure (e.g., afterfiltering) brain activity during actions such as walking in the wholebrain or in specific regions. Optionally, EEG is used to detect minimalchanges in brain activity secondary to focal activation and/ordepression of neuronal discharge. Based on the observations of decreasedblood flow to the frontal lobe during FOG, it is expected that therewill be focal frontal slowing or as called in EEG terms, theta or deltaactivity over the frontal lobe. Abnormal EEG activity can also becharacterized by hyper or hypo synchronization of brain electricalactivity in a specific area. EEG activity has been shown to be able todetect not just the movement potential but also the preparatorypotential that comes before the actual movement is executed, which maysupport the use of EEG for prediction and detection of an actual event.

In an exemplary embodiment of the invention, continuous scalp EEGmonitoring during walking, for example, by the Oxford ambulatory EEGmonitoring system, is used to differentiate between normal stepping andFOG or pre-FOG state by change in background EEG activity over thefrontal lobe bilaterally. In an exemplary embodiment of the invention,in the 1-3 seconds prior to the FOG itself and/or during the actualfreezing episode, slowing of the background activity will be detected byautomated frequency analysis system which is already present in theOxford system. The system will be able to learn (e.g., using machinelearning methods as known in the art) the normal locomotion of thesubject treated and recognize the FOG as a significant change from theregular background. Similar detection may be applied for falls and/orother gait abnormalities.

In an exemplary embodiment of the invention, EEG measurement is used tospecifically detect increase or decrease of activity in frontal lobesand/or motor regions, for example, based on changes in intensity (e.g.,at certain frequency bands).

A potential advantage of EEG is its integration into an ambulatoryand/or implanted device.

Optionally, acceleration and/or gyroscope sensors, for example, as shownin FIG. 4B are attached to, for example, the ankles of the patient torecord the gait patterns of the participants and their reaction to theVR stimulus. Exemplary Shimmer sensors 420 are provided bywww(dot)shimmer-research(dot)com. The sensors contain 3-axisaccelerometers and 3-axis MEMs Gyro that record data at a sampling rateof 100 Hz via Class 2 Bluetooth Radio, and optionally serve to close theVR simulation loop. The shimmer sensors were use to collect gaitmeasures and the reaction of the participant to the VR scene such as achange in pace or cadence before obstacles, correction patterns andmissteps.

Optionally, sensors 420 include an external dock 426, a reset button 424and/or indicators, such as LED indicators 422. In an exemplaryembodiment of the invention, data from the Shimmer sensors is channeledto Matlab software, running on a laptop computer (e.g., 412), thatperforms real-time synchronization between the 2 shimmers (on bothankles) and runs an algorithm for detecting falls and/or near falls,based on, for example, the fall Index (FI), described below. Optionally,the laptop running the FI algorithm is connected to a computer runningthe virtual reality simulation using a network cable and TCP protocol.When a fall or near fall is detected, a signal is sent to the virtualreality simulation, enabling the simulation to record the preciselocation and time of the detected event within the simulation. Thesystem also records the leg on which the event was detected first (thesensor that detected the FOG threshold), the speed at which the patientwas walking, the type of trail e.g., the conditions of the VRsimulation, the type of obstacles used, if any, and/or the type of FOGprovocations provided by the simulation at the time of the event.

FIG. 4C shows an aerial visual representation of a VR simulation trailand a recorded fall event within the trail. The time of event within thesession is recorded as well as the location on the path (represented bythe white x, in this case on the narrow bridge over the virtual river),the type of trail used and the challenges provided (in this case, e.g.,day (or night) condition and narrow pathways) and the first leg theevent was detected by the shimmer sensors (in this case the right leg).This information could be meaningful as if the pattern occurs always onthe same leg, it may suggest asymmetrical use or weakness of oneextremity that can be addressed by treatment.

While this implementation may use a dedicated VR simulation, in otherembodiments, the VR simulation is part of a commercial game. Optionally,the game is modified to generate a desired rate of challenges, forexample, by creating narrowings in pathways. Alternatively, a game isselected with sufficient challenges and the patient simply plays thegame, while the system tracks which challenges affected the patient andin what manner.

As noted above, for validation and/or other uses, additional sensors maybe used. For example, miniaturized physiological sensors (NeXusMindMedia BV the Netherlands) may be attached to the patient's chest tomonitor the patient's heart rate during different scenarios and walkingconditions and physical and mental stress. Wireless Functional NearInfrared Spectroscopy sensors (fNIRS-PortaLite, Artinis, TheNetherlands) may be placed on the patient's forehead to assess bloodoxygenation in the frontal lobe during the test. These signals mayreflect frontal lobe activation in response to different stimulationsand/or allow the assessment of cognitive function during a fall eventand/or other gait challenges. Optionally, these two modalities were usedfor validation of the fall events. Optionally or alternatively, they canbe used as an option in the diagnostic system to provide additionalinformation to the clinician. In an exemplary embodiment of theinvention, all systems and sub-systems are synchronized and the sessionswere videotaped to allow for further analyses of the fall events.

VR Simulation

In an exemplary embodiment of the invention, the VR simulation isdesigned specifically for this use and written in OGRE (Object-OrientedGraphics Rendering Engine) which is a scene-oriented, real-time,flexible 3D rendering engine, programmed in C# using Direct3D and OpenGLas the graphic libraries. The simulation optionally requires processingof multiple stimuli simultaneously. The VR scene consisted of an outdoorboardwalk on which different obstacles were placed. The patients wererequired to walk on the treadmill while negotiating the obstacleswithout hitting them. These mobility skills required decisions aboutstep amplitude in two planes (vertical obstacles that required a highstep and horizontal obstacles which required long steps) coordinatedwith walking behavior. See, for example, FIG. 4D which shows twoexamples of virtual scenes designed to provoke gait problems such asfalls. The patient's movement is represented by the shoes on the screen.These provide feedback as to movement, success or failure in negotiatingthe obstacles and a sense of presence within the VR simulation. A morecomplete avatar may be used as well. Obstacles presented were eithervertical (top represented as a hurdle) requiring high clearance, orhorizontal (bottom represented by a black muddy spot) requiring a longstep. In order to successfully negotiate the obstacles, patients need toplan the correct response, plan the timing of passage and anticipate thespeed required for performance. If successful they receive points on thescore board shown on the top of the screen. If an error occurs and thepatient touches the obstacle, a red light appears and the attempt isscored as collision. The amount of obstacles changes depending on thedifficulty level of the trial and the speed at which they were walkingat. The decision as to the side of appearance (right or left leg) isoptionally chosen based on the more impaired side, e.g., based on PD(Parkinson's Disease) symptoms (e.g., with 75% of the obstaclespresented to the more affected side).

These decisions are optionally made more difficult using distracterssuch as changes in lighting and moving objects in the simulation and/orby adjustment of the frequency and/or size of the virtual obstacles.This allows varying the cognitive load independently of the gaitcomplexity and/or potential fall triggers. Optionally, the sceneincludes gait challenging features such as bridges over rivers, narrowpassages, tunnels, a cave, distracters and/or lighting effects. FIG. 5shows two examples of virtual challenging scenarios. These provocationsincluded features such as bridges over rivers (see also FIG. 4D),tunnels (top) or a cave, narrow passages (bottom) distracters and/orlighting effects, diminishing the visual field or obstructing the viewto make planning a higher performance in the obstacle course morechallenging and/or perhaps to elicit fear of falling and a more cautiousgait, reflecting situations that could occur in everyday life. In anexemplary embodiment of the invention, such features are manipulatedwith respect to, for example, one or more of their frequency ofappearance, size and/or location according to the individual patient'sneed and/or the difficulty level desired for a trial. In general, thesefeatures may be used to introduce challenging situations that may causean illusion of instability and fear of falling.

In an exemplary embodiment of the invention, the environment imposes acognitive load requiring attention, planning and response selection aswell as processing of rich visual stimuli involving several perceptualprocesses that have been associated with falls. The VR provides visualand/or auditory feedback upon success or error of crossing the obstaclesand/or if a fall/misstep occurs; this feedback is optionally used aspart of the therapeutic option. The system optionally providesinformation as to the location of the fall, the timing of it, the leg onwhich it was first detected, and/or the duration of the event.

In an exemplary embodiment of the invention, if the system detects gaitpatterns that are known to increase risk of falls (e.g., missteps,shuffling), a visual and auditory feedback may be provided on screen andthe location, timing, the leg on which it was first detected, and theduration of the event may be recorded.

Data Processing and Extraction

As noted the system as described herein is optionally used for one ormore of 1) assessing the possibility of identifying individuals withrisk of falls using the system, 2) validating the fall risk algorithmsagainst physiological measures, and/or 3) quantifying the risk for fallsby different parameters of performance. Below is a description ofexemplary methods used for data processing usable for these aims.

Gait Data

Gait data is optionally extracted from the accelerometers in the shimmersensors. Average gait speed and stride time are optionally evaluated forwalking trials. Data collected by the accelerometer are also optionallyused to assess measures of rhythmicity and/or stability known to beimpaired in patients known to be fallers and those with increased riskof falls. These included, for example, one or more of measures ofvariability, consistency and symmetry.

-   -   Spectral analysis of the calibrated acceleration signal in the        locomotion band (0.5-3.0 Hz) is optionally used to assess        measures of variability of the signals during gait on the        treadmill without obstacles. The peak amplitude the width and        the slope of the dominant frequency in the anterior-posterior        direction are extracted from the raw signal; a sharper and        narrower peak may reflect a more consistent, rhythmic, and        healthier gait pattern, e.g., reduced gait variability and/or        lower stride-to-stride fluctuations.    -   A symmetry ratio is optionally calculated based on the        difference between acceleration of the right and left sensors        during the no obstacle condition.    -   A Phase Coordination Index (PCI) is optionally calculated from        the acceleration signal by determining the stride duration of        one foot in the gait cycle (defined as) 360°, where the relative        timing of the contra-lateral heel-strikes defined the phase        which is represented by y (ideally, φ=180 for every step). The        sum of the coefficient of variation and the mean absolute        difference between φ and 180° is defined as the PCI,        representing variability and inaccuracy, respectively.

Gait Abnormalities and Misstep Detection

In an exemplary embodiment of the invention, a Fall Index (FI) iscalculated from spectral considerations. Wavelets and signal processingare optionally used by identifying specific patterns in the accelerationsignal that correspond with high frequency and increased power andregarded as an indication of a misstep. In an exemplary embodiment ofthe invention, the FI reflects a ratio between the power in gaitfrequencies (e.g., 0.5-3.0 Hz) and the high gait frequencies (3-8 Hz).In an exemplary embodiment of the invention, calculation of these twomeasures is performed continuously and/or for each leg separately. In anexemplary embodiment of the invention, a real-time running window isapplied to the data from the vertical axis (perpendicular axis to theground). The size of the chosen window is optionally 1.2 seconds, as anexample of a tradeoff between better frequency analysis and minimallatency but a wider range of windows (smaller e.g., 0.6 seconds or lessor wider e.g., 1.5 sec or more) may also be used. The information fromeach window is transformed using, a spectral transform, such as FastFourier Transform (FFT) and the distribution of the signal, in thefrequency domain, is calculated.

In an exemplary embodiment of the invention, a low value (e.g., comparedto a peer group) reflects a strong gait while high values suggest adisturbed or pathological gait. When a change in gait pattern isdetected, a signal is optionally sent to the virtual reality simulation.The precise location and time of the occurred event within thesimulation, the leg on which the event was detected first, the speed atwhich the participant was walking in, the type of trail and/or obstaclesand provocation provided by the simulation at the time of event areoptionally recorded by the VR simulation. The data is then optionallyextracted from both the sensors and the VR simulation for furtheranalysis.

Cascade Method to Detect Falls and Near Falls

In this method, a first method (e.g., SFA) is applied on the windows andthen a second method (e.g., WWA) is applied on the windows notidentified. Preliminary results suggest a hit rate of 85.7% and aspecificity of 96.8%. Alternatively, the WWA and SFA methods may beseparately applied or other methods may be combined with them. A briefdescription of the WWA method and the SFA method, in accordance withexemplary embodiments of the invention, follows.

Window-Wise Analysis (WWA) Description

An optional preliminary stage to running the misstep detectionalgorithm, is extraction of gait segments, since missteps by definitioncan only occur while walking. Good gait detection can substantiallyreduce false alarms generated by noise. While manual annotations may beused to locate gait segments, an automated Gait Detection (GD) algorithmmay be used.

Exemplary Gait Detection

Acceleration signal from the lower back has a repeatable pattern infrequencies between 0.5-3 Hz for normal walk. The signal is noisy due tovarious reasons such as tremor of the patient, different types of walkand placement of the sensor. In an exemplary embodiment of theinvention, to minimize false detection of gait, the signal is filtered.

Next, a running window of (for example) 5 sec in length is applied onthe vertical and anterior-posterior acceleration signal. The data ateach window is convolved with one cycle of (for example) 2 Hz sinusoidalsignal that represents a cycle of gait in the filtered data. Theresultant signal enables detection of gait by searching for local maximawhich represents one gait cycle. Only windows at which 2-15 steps aredetected are considered as walking. This range was chosen since gaittypically exists in the range of 0.5-3 Hz where 0.5 Hz means a step eachtwo seconds and 3 Hz means 3 steps a second. A 5 seconds windowtherefore contains 2-15 gait cycles.

Misstep Detection

The data that contains gait is divided into 5 seconds windows. Thesensor placed on the subjects may sometimes be tilted or shift slightlyduring the trials. In order to remove artifacts caused by such movement,a normalization process is optionally applied to each window,subtracting its mean.

In an exemplary embodiment of the invention, the method identifiesirregularities in the gait in each window which may suggest a misstep.The procedure is optionally performed both on the vertical (V) andanterior-posterior (AP) acceleration axes. Each window is divided intothree segments. For the vertical axis, the maximum in each segment iscalculated, resulting in 3 maxima values. If the highest maximum isgreater than 1.5 times the second largest of these maxima then thewindow is classified as suspected misstep (SM). Otherwise the windowprobably describes normal gait for which the difference between the 3maxima is expected to be small.

A similar process is optionally performed on the anterior-posterior (AP)axis with two differences. The first is that the minimum is calculatedat each segment and the second is that the distance between the lowestminima and 1.5 times the highest minima is checked.

At the end of this stage, a union between the two decisions isperformed, meaning that at least one of the decisions on the AP orvertical (V) should be SM in order to declare it as SM, Otherwise it isdeclared as non-misstep and will not be further examined.

In order to determine that the irregularity happens only in the 5seconds window and it is not a change in gait due to obstaclenegotiation, or start or end of gait, a wider environment around thewindow is optionally examined. This environment can be an extension ofthe window by half a window width in each side. In this step it ischecked that the maximum of V acceleration, V, Y and Z gyros, and theminimum of ML and AP acceleration, computed in the extended window,occur within the original window, and not in the extensions. Extremaoccurring within the extensions rather than the original window, couldimply that the extrema are not due to irregular gait.

In the case that the minimum is outside the original window, but insidethe extended environment, it is optionally further examined by adifferent window. After establishing that it is indeed an abnormalitythat exists within the window, a closer examination, around thesuspected misstep, is optionally performed to determine if it is amisstep or not. For that a smaller window may be built around thebeginning point of the misstep (BPMS). Since the time before that pointis less relevant than the time after it, which may include a recoverymechanism, the optionally window is built by taking 1.25 sec before theBPMS and 2.5 sec after it.

In the new window the 2 lowest minima are detected. If the higher minimatimes 1.3 is greater than the lower minima than it is declared as not amisstep by this feature.

A similar procedure is optionally performed for all the 6 featuresdescribed above and their decisions enters a Majority Rule decisionmodule (for example). If 3 or more of the features declare the window asa misstep than it is labeled as such.

This method appears to detect most of the missteps, but also reportssome false alarms. Optionally, at least some of the false alarms arefiltered out by using 2 thresholds. The first is a requirement that afiltered signal of V acceleration exceeds the value of (for example) 0.4(Band pass filter 0.5-4 Hz) and the second is that the filtered ML-Gyroexceeds the value of (for example) 25 (Band pass filter 4-20 Hz).

Results

Running this algorithm on laboratory data achieves 71.7% Hit ratio and96.3% specificity. It should be noted that Hit ratio is used instead ofsensitivity because missteps may occur over more than one window and forthis method detection of at least one of them is sufficient.

SFA Description

In an exemplary embodiment of the invention, gait detection, for exampleas described above is optionally applied to the laboratory data toautomatically detect segments of gait.

A running window of (for example) 5 seconds is computed for (forexample) 6 signals. At each window a series of features are calculatedfor each subject and a feature vector is created. For each featurevector the maximum is detected and points which exceed a threshold,derived from that value, are marked. It should be noted that althoughthe thresholds are computed by the same computational method for allsubjects, the resultant threshold value differs.

In an attempt to improve the specificity, various features wereextracted from the signals, as will be described below. In total, over60 features were extracted from the 3 axes of acceleration and the 3axes of gyroscopes. The features included parameters from time andfrequency domain including wavelets and statistics. Eventually, only afew of these features were utilized. The features are:

Acceleration Features

The acceleration features are root mean square (RMS) of AP accelerationand the signal vector magnitude (SVM) of 3-axes standard deviation, SD.Extreme changes in those two features indicate irregularity in the gait.

Frequency Features

These features are divided into 2 parts—features extracted from gaitfrequency (0-3 Hz) and from higher frequencies (3-10 Hz) at whichmissteps may be observed.

ske5W=Skewness of the ML-Gyro in gait frequencyske1M=Skewness of the V-Acceleration in High frequenciesske2M=Skewness of the ML-Acceleration in High frequencieskur2W=Kurtosis of the ML-Acceleration in gait frequencykur5W=Kurtosis of the ML-Gyro in gait frequencykur2M=Kurtosis of the ML-Acceleration in High frequencies

DWT Features

There are several families of DWT, here used is the 2nd level of‘db4’-cAA. The discrete wavelet features that were used are:

DWTkur2=Kurtosis of the ML-Acceleration DWTkur3=Kurtosis of theAP-Acceleration DWTkur5=Kurtosis of the ML-Gyro DWTkur6=Kurtosis of theAP-Gyro DWTske3=Skewness of the AP-Acceleration DWTske2=Skewness of theML-Acceleration DWTske5=Skewness of the ML-Gyro

Three methods are optionally used for calculating thresholds. Differentmethods may be used for different features. Optionally, all the methodsare based on finding global peaks for each subject. The formulae usedfor each threshold are:

Threshold 1=amplitude of the 4th strongest peak

Threshold 2=amplitude of the 2nd strongest peak

Threshold 3=1.25*(mean+std)

For example, Threshold 1 is used for RMS of AP (RMS-AP), threshold 2 isused for STD 3D and Frequency features, and threshold 3 is used for DWTfeatures.

By analyzing an SVM of 3-axis standard deviation SD it is possible tosee that this feature can separate well missteps from non-missteps andusing thresholds it is possible to reject many examples which are notmissteps. Optionally, the threshold is on the distance from the origin.

Marked windows from those features are labeled as “suspected missteps”.Union between these features detect the majority of missteps (over 90%)but also results in many false alarms (FA). To identify missteps withhigher certainty additional features such as skewness and kurtosis areoptionally used by applying the same mechanism. These features werechosen because they can identify many of the FA while almost notreturning any hits. The following table shows the features' performance:

Feature Hit ratio Specificity FA RMS-AP 67.85 95.34 151 STD3D 89.2891.28 295 RMS-AP + STD3D 96.43 90.3 329 ske5W 3.57 95.78 136 ske1M 096.41 115 ske2M 0 96.53 111 kur2W 0 96.77 103 kur5W 7.14 95.93 131 kur2M0 96.47 113 DWTkur2 0 93.49 215 DWTkur3 0 90.61 320 DWTkur5 0 92.15 263DWTkur6 3.57 90.14 338 DWTske3 0 86.43 485 DWTske2 0 88.74 392 DWTske5 087.46 443

The union of the first two features, RMS-AP and STD3D, is used to labelwindows as “suspected missteps” and union of the other features is usedto reduce FA. Results

Running this algorithm on laboratory data achieved 85.7% of Hit ratioand specificity of 95.4% and FA of 147.

Anticipatory Postural Adjustment

In an exemplary embodiment of the invention, it is expected that asubject makes anticipatory postural adjustments (APAs), for example,changes in center of gravity (COG) and center of pressure (COP).Optionally, such APAs are detected, for example, using cameras and/ormovement sensors and used, for example instead of or in addition toother physiological measures, to predict and/or identify gaitabnormalities such as FOG.

In an exemplary embodiment of the invention, an APA is measured byquantifying the COP and/or by measures of trunk movements usingaccelerometers and/or gyroscopes carried on the belt or other positionsthat allow for estimation of the COP and/or COG. By challenging thesubject in the VR system, it may be possible to detect early, mildand/or subclinical APA disturbances which may also optionally be used asmarkers for FOG. As noted herein, early detection allows to implement anearly and potentially protective interventional approach to delay,reduce and/or prevent FOG and/or other functional disorders.

In an exemplary embodiment of the invention, APA detection is used fordriving a cueing system for treatment of FOG and/or other gaitdisorders.

In an exemplary embodiment of the invention, APA detection is used as amarker for the usefulness of interventional programs with drugs, deepbrain stimulation or physical rehabilitation methods.

In an exemplary embodiment of the invention, APAs are used to predictFOG, for example, before turns, when starting to walk and/or even during“open runway”, usual walking.

In an exemplary embodiment of the invention, APAs are used as a targetof training, for example, after training, larger APAs may be expectedfor some patients.

In an exemplary embodiment of the invention, APAs are used to diagnose apatient, for example, by seeing if and how APAs change and/or aredelayed as a function of the type or other parameter of challenge used.

In an exemplary embodiment of the invention, VR simulations are modifiedin real-time to cause a desired APA (e.g., a certain COP). Optionally,the simulation is modified (e.g., various scenarios tried, intensitychanged) until a desired APA is detected and/or failure is decided.

The abstract of Exp Neurol. 2009 February; 215(2):334-41. Knee tremblingduring freezing of gait represents multiple anticipatory posturaladjustments. Jacobs J V, Nutt J G, Carlson-Kuhta P, Stephens M, Horak FB reads as follows: Freezing of gait (FoG) is an episodic, briefinability to step that delays gait initiation or interrupts ongoinggait. FoG is often associated with an alternating shaking of the knees,clinically referred to as knee trembling or trembling in place. Thepathophysiology of FoG and of the concomitant trembling knees isunknown; impaired postural adjustment in preparation for stepping is onehypothesis. We examined anticipatory postural adjustments (APAs) priorto protective steps induced by a forward loss of balance in 10Parkinson's disease (PD) subjects with marked FoG and in 10 controlsubjects. The amplitude and timing of the APAs were determined fromchanges in the vertical ground-reaction forces recorded by a force plateunder each foot and were confirmed by electromyographic recordings ofbilateral medial gastrocnemius, tibialis anterior and tensor fascialatae muscles. Protective steps were accomplished with a single APAfollowed by a step for control subjects, whereas PD subjects frequentlyexhibited multiple, alternating APAs coexistent with the knee tremblingcommonly observed during FoG as well as delayed, inadequate or nostepping. These multiple APAs were not delayed in onset and were ofsimilar or larger amplitude than the single APAs exhibited by thecontrol subjects. These observations suggest that multiple APAs producethe knee trembling commonly associated with FoG and that FoG associatedwith a forward loss of balance is caused by an inability to couple anormal APA to the stepping motor pattern.

In an exemplary embodiment of the invention, APAs are measured using aforce platform and/or using center-of-pressure dynamics (e.g., forcesensitive insoles or the accelerometers described above, which canreflect movement of the body's center-of-mass, which will reflect alsothe APA).

The inventors have also discovered that, based on a study 29 patientswith Parkinson's disease (PD), freezing of gait episodes during turnsare marked by multiple failed postural adjustments. These posturaladjustments are typically seen as Anticipatory Postural Adjustment atgait initiation (e.g., before the person starts to walk). However, usingmeasures of Center of Pressure (COP) Dynamics, they can also bequantified during turning and/or during straight line walking.

The obstacles placed in front of the subject generally also require aform of an APA (e.g., shifting of the center of gravity from one foot tothe other to allow for sufficient clearance of the virtual obstacle). Bychallenging the subjects with these virtual obstacles (e.g., ofdifferent lengths and/or heights), the APAs/COP in response (e.g.,before and/or during) can be measured. Possibly, in a healthy subject,the APA size will be related to the size/height of the obstacle.Optionally or alternatively, if/how these APAs change during FOG ismeasured. This can give another measure of FOG pre-disposition andpossibly further enhance the ability to grade FOG severity, to predict,and/or to measure the response therapy.

In an exemplary embodiment of the invention, APAs are treated as areother measures, such as BCG. For example, APA is included as one of theweighted features in the scoring for FOG.

It is noted that in some embodiments, the APAs are measured on theground (e.g., if patient is walking on ground towards a very largescreen and/or wearing goggles) and in other embodiments APAs aremeasured on motion devices, such as treadmills and/or bicycles.

In an example of on ground VR display, a patient follows a standard labcourse, such s walking along a corridor, and goggles are used to injectobstacles into the course and/or provide other loads as describedherein.

Quantifying Fall Risk

The Fall Risk score is a composite measure optionally based on two ormore of the number of events detected by the system during the test,gait parameters reflecting abnormal patterns (e.g., stride timevariability (CV), PCI, symmetry), the response to the VR provocations,number of errors on obstacle crossing, the cost of environmentalfeatures (e.g., determined as stride time in trial 3-stride time intrial 4) and/or the cost of cognitive load on performance (e.g.,determined as stride time in trial 5 stride time in trial 4).

Table 1 (FIG. 6) shows how such a composite score is optionallycalculated. The composite score described herein provides the subjectwith an overall, composite score, based on the combination of multiplecomponents. In addition to this single summary measure, the clinicianmay receive more detailed information that describes fall propensitybased on performance in the VR system. Using a weighted analysis, allmeasures are optionally assessed under 4 levels (or a smaller or largernumber of levels): gait changes, cost and provocations, obstacleavoidance, adaptation. Each of these levels optionally receives aseparate score and then all levels may be evaluated to provide a fallrisk score based on, for example, a 4 point Likert scale.

It is noted that in other methods, fall risk is not quantified and isdescribed based on either clinical performance based measures such asthe timed up and go (in which a person's performance is considered highrisk or low risk) or on measures of gait which can reflect a problem anda risk for falls that is specific to gait (high gait variability). In anexemplary embodiment of the invention, the multifaceted levels thatincrease the risk of falls and therefore the definition of risk is takeninto account. In some embodiments of the invention a multifactorialdefinition is used which takes into consideration many levels of risk.For example, high risk is identified as frequent changes in gait patternor detected missteps even with situations with low level provocation, orsimple environments, and a deterioration in gait pattern in response toeven simple cognitive challenges resulting in high gait variability andasymmetry.

In an exemplary embodiment of the invention, the score is a linear sumof considerations, each weighted, for example, according to patientcharacteristics, for example, based on a library of control subjects.Other forms of score formula may be used as well.

While not limited to the following definitions, some embodiments of theinvention define a fall as ‘unintentionally coming to the ground or somelower level and other than as a consequence of sustaining a violentblow, loss of consciousness, sudden onset of paralysis as in stroke oran epileptic seizure’ (Kellogg 1987). In an exemplary embodiment of theinvention, a misstep or near fall is defined as a loss of balance orfoot hold with the ground during gait which did not result in a fall dueto the ability to overcome it/control it/compensate for it. In anexemplary embodiment of the invention, the use of a harness preventsactually falling but allows falls and missteps to be estimated based ontrajectory before harness stops patient and/or based on change in gaitpattern (e.g., including the higher frequency) can indicate that thepattern is of a misstep.

Experiment

The above-described implementation was used in an experimental study, asdescribed below and shows the ability of the proposed VR system, inaccordance with some embodiments of the invention (e.g., FIGS. 4A-5) toprovoke and detect fall episodes under safe environmental conditions.The system is able to provoke fall episodes on the treadmill. The systemis able to sensitively detect these fall episodes, and using the systemfeatures is able to quantify and/or configure a severity score that canbe used to diagnose and later provide care to patients with risk of orexisting fall events. It is noted that features described with theexperiment may be used, as desired with other embodiments of theinvention from the one used in the experiment.

Participants

The developed system was tested on 3 healthy elderly subjects with ahistory of falls (mean age 71.7±7.5 yrs) and one healthy older man (67yrs) with no history of falls who served as a control subject. Allsubjects reported no distinct medical history that may have contributedto the occurred falls, they were all community ambulators and wereindependent in activates of daily living. Participants were excluded ifthey had substantial cognitive deficits (scored <21 on the MontrealCognitive Assessment scale), unstable heart disease or suffered fromsevere depression.

Procedures

After signing an informed consent, demographic information and medicalhistory were collected by a researcher. Prior to testing the system, abaseline assessment was conducted to evaluate gait over ground. Gaitspeed was measured over 10 meters. This information was imperative asthe treadmill speed during the system's evaluation was set for eachparticipant based on their over ground walking speed. In otherembodiments, speed may be set during trial and/or matched to an actualwalking speed on a continuous and/or semi-continuous basis. Participantswere then fitted with the sensors (Shimmer, Nexus and fNIRS) for testingwith the system. The test included 5 walking conditions each of 4minutes for a total of 20 minutes of walking. Rest breaks were givenbetween the trials. The trials varied with each walking conditionfocusing on a different component that may influence gait and fall risk.

Trial 1—Difficult: high level of difficulty, maximum amount ofobstacles, maximum amount of challenging scenarios (tunnels, cave,bridges, and narrow passages)Trial 2—Moderate: medium level of difficulty, moderate amount ofobstacles, minimal amount of challenging scenarios, low environmentalcomplexityTrial 3—Environment: high level of difficulty, moderate amount ofobstacles, minimal challenging scenarios, high environmental complexity(obstructed visibility, night)Trial 4—Gait challenges: low level of difficulty, no obstacles, maximumchallenging scenarios (tunnels, caves, bridges and narrow passages)Trial 5—Cognitive: high level of difficulty, moderate amount ofobstacles, low amount of challenging scenarios, additional cognitivetask (on top of walking with the VR simulation, the participants wereasked to perform a verbal fluency task).

In an exemplary embodiment of the invention, these specifictrials/levels were chosen as they cover the most common causes formissteps and falls (e.g., tripping/environmental, gait impairments,cognitive and sensory motor integration). The parameters in each of thetrials could be quantified (i.e., number of obstacles placed, thedistance between them, frequency of appearance, number of provokingchallenges etc). The number of provocations and obstacles within a 4minute trail depends on the speed the subject is walking in and can varyon average, for example, between 25-40 (when gait speed is between 0.8cm/s-1.4 m/s and an obstacle is placed on average every 6 steps).

Validation with Physiological Measures that Possibly Contribute to FallRisk

The following methods were used in order to validate the detectionalgorithms and to also assess several physiological measures that have arole in falls.

-   -   a. During the tests, an experienced clinician observed the        subject and annotated any missteps that occurred. The report        included both descriptive measures of severity and time of        event. In addition, all trials were videotaped. Another        experienced researcher was asked to review the recorded videos        and annotate time of misstep events based on the video        recordings. These were then compared to the events detected by        the system and by the researcher who attended the tests.    -   b. Miniaturized physiological sensors (NeXus MindMedia BV the        Netherlands) were attached to the person's chest to monitor the        person's heart rate (HR) during different scenarios and walking        conditions and physical and mental stress to try and identify if        any changes occurred that could indicate an event. As some falls        occur due to syncope, optionally, these sensors can also be used        to aid the clinician in the diagnosis and evaluation of possible        risks for falls in particular individuals. The wireless NeXus        sensors transmitted data in real-time to a computer using        Bluetooth technology. Using designated software, heart rate and        inter-bit-intervals were extracted from the data collected by        the sensors in all gait trials.    -   c. Wireless functional Near Infrared Spectroscopy (PortaLite,        Artinis, The Netherlands) was used to assess changes in frontal        lobe blood flow during gait to shed light on cognitive function        during challenging situations that increase the risk for falls.        The system uses Near Infrared Spectroscopy to measure local        tissue saturation as well as oxy, deoxy and total hemoglobin        concentrations in the frontal lobe during activity. Oxy and        deoxy hemoglobin data (in units of micromole/liter) during all        gait trials were extracted using Matlab software. As noted,        optionally, these sensors can also be used to aid the clinician        in the diagnosis and evaluation of the possible causes of        increased risk of falls in a particular participant such as        cardiac syncope, arrhythmias and subtle cognitive impairments        (e.g., as observed by decreased frontal lobe blood oxygenation).

Data from both HR and FNIRS were examined throughout the gait trials andchanges and events were assessed and verified according to the videorecordings. The signals were then examined for an interval of 10 secondsbefore and after a detected event to observe any changes in activation.The signals were then compared to no-event and no-obstacle trials.

Data Analysis

Data was examined for normalcy and descriptive statistics were extractedfor all gait measures. Validation data were analyzed based on timeseries across all detected events. Quantification data were analyzed foreach person as a case study.

Results Diagnostic Capabilities

Three healthy older adults with a history of falls and one controlparticipant participated in this study designed to demonstratediagnostic capabilities. The three participants reported falling atleast twice in the year prior to the study rendering them as “multiplefallers.” All participants were functionally active and living in thecommunity. Table 2 (FIG. 7) provides the subject's descriptivecharacteristics. It should be noted that subject number 2 is the controlparticipant (no history of falls). Gait Data Mean gait speed duringover-ground walking was 1.4±0.1 m/s. Gait speeds on the treadmill wereset 20% slower to allow for obstacle negotiation as well as try toinduce misstep events. Treadmill speeds ranged between 1.1-1.2 m/s andwere not changed between the different trials, to maintain consistencyof gait and evaluate strategies of walking in the different conditions.FIG. 8 demonstrates the acceleration signal of the gait of subject 4during the obstacle condition (trial 1). The top graph represents thesignal collected in the anterior-posterior axis (AP), the middle signalrepresents the movement in the vertical axis (V) and the bottom signalrepresent movement in the medio-lateral direction (ML). The ellipsescircles are examples for location of obstacles within this time frame.It should be noted that the acceleration increases when the personattempted to cross the obstacle. In the ellipse on the right the subjectwas attempting to cross a horizontal obstacle, which increased his step.

Average stride time in the no-obstacle condition was 1.21±0.42 sec forthe fallers and 1.16±0.31 for the control subject. Stride time did notchange during the obstacle conditions (1.23±0.63 sec and 1.24±0.58 sec).However during the challenging walks (environmental complexity andcognitive load), all participants demonstrated shorter stride time(1.15±0.45 sec and 1.14±0.61 sec respectively), suggesting acompensatory strategy in challenging situations.

The costs of adding obstacles, environmental features or a cognitivetask were calculated as the difference in stride time between theno-obstacle conditions and the evaluated condition(environmental/cognitive/obstacle). The three fallers demonstrated thelargest differences in the cognitive task, which could be considered asthe ‘dual task’ effect (0.53 sec) however, interestingly the controlparticipant did not show a dual task effect with only a difference of0.07 sec in stride time between the trials. In the environmentalchallenge condition, the participants were asked to walk in a darkenvironment with low visibility. Here all 4 participants demonstratedthe same effect with a decrease in stride time of an average of 0.19sec. These findings suggest that during decreased visibility walking,older adults change their walking pattern to reflect a more cautiouspattern, which could perhaps be considered a coping strategy for adifficult situation. These findings may also reflects the difficultythat older adults have in these situations that could increase fear offalling and result in individual restrictions of movement in thesesituations (such as not going out at night, not getting up during thenight because of the fear of falling in the dark etc.) By identifyingsuch a behavior of difficulty one can provide treatment andinterventions to relieve such fears and improve performance andconfidence in walking. The findings also support the reports in theliterature that fallers have more difficulties with DT tasks.

A symmetry ratio was calculated as the difference in stride time betweenthe sensors worn on the right and left legs during the no obstaclecondition and reflected as percent. This ratio reflects a difficulty incontrolling gait evenly on both legs. Participants 1, 2 and 4demonstrated an almost perfect symmetry (98%, 99% and 98% respectively).Subject 3 demonstrated a high inter-limb asymmetry with a ratio of only50% suggesting a less coordinated gait. This possibly reflectsdis-coordination or weakness of one side which could result in increasedrisk of falls.

Measures of consistency in walking were also evaluated. Coefficient ofvariation (CV) and PCI were calculated from the gait during theno-obstacle condition. For both measures, the closer the values were tozero, the more consistent the gait rhythm, suggesting a less impairedgait pattern with more intact bilateral coordination. In addition, theamplitude and width of the dominant frequency throughout the 4 minuteno-obstacle walk were examined using spectral analysis. In table 3 (FIG.9), are presented the results of the 3 subjects compared to the controlsubject. A sharper and narrower peak reflects a more consistent,rhythmic, and healthier gait pattern, i.e., reduced gait variability andlower stride-to-stride fluctuations.

FIG. 10 shows a raw acceleration signal and spectral density of thefrequency band of the gait of a faller (subject 4) compared to thecontrol participant (subject 2). Note the higher amplitude and narrowersignal in the control subject then subject 4 (faller) suggesting a morecontrolled and less variable gait pattern. The signals represent 10seconds of gait in the no-obstacle trial. The effects of the variouschallenging conditions on these measures can also be included as anothermeasure of fall propensity. Gait abnormalities and Misstep detection Atotal of 31 events were detected by the system; 6 of those events weredeemed as missteps by using the video recordings. Sixteen of the 31events collected were recorded during the trials of subject 4. Allevents reflected changes in frequency of the signal during walkingwithin the window stipulated by the FFT. In further analysis thesechanges reflected missteps but also increased steppage gait whencrossing obstacles, overshooting and undershooting targets, andirregular steps produced as compensation for challenges presented by thesystem. All events lasted less than 2 seconds, perhaps because of thecontinuous motion of the treadmill belt and the need for the subjects totake a step forward. This may predispose patients to take larger stepsand have legs moved by treadmill and thus avoid falling. Nonetheless,even on the treadmill, with virtual obstacles, missteps could bereliably provoked.

FIG. 11 shows the acceleration signal of the gait of subject 3 duringthe no-obstacle condition (trial 4) and the signal from the cognitivetrial (5) in which a misstep was detected. The top graph represents thesignal collected in the anterior-posterior axis (AP), the middle signalrepresents the movement in the vertical axis (V) and the bottom signalrepresent movement in the medio-lateral direction (ML). The ellipseindicates the misstep detected by the system.

Validation

Events detected by the sensors were compared against the recordings doneby the researcher in the test and the identification of events usingvideo recordings. There were 31 events detected by the system, 27 ofthose were corroborated by the researcher observing the tests. From thevideos, only 26 were deemed as gait corrections over obstacles, misstepsor changes in gait pattern that could result in a fall if not supported.Although the events were very short and some were not easily observed onthe video, there was a high agreement between the researchers and theautomated system. This finding is encouraging as it demonstrated thehigh sensitivity and specificity of the system.

To further validate the system, physiological measures (e.g., fNIRS andHR) were used as well. FIG. 12 shows the raw signal from the fNIRS anddemonstrates the raw signal from the fNIRS during a misstep event. Thelight line represents de-oxy hemoglobin and the dark line represents oxyhemoglobin. The time series reflects 40 seconds of gait. The increase inoxy hemoglobin in the frontal lobe during the event may suggest that thebrain is circumventing blood flow to the frontal lobe in order to motorplan a strategy of recovery from the event. When the misstep occurs,there is an increase in blood flow in the frontal lobe. The increaseblood flow may be a reflection of the need for cognitive awareness andplanning a recovery strategy to quickly come into play.

This pattern was not observed during normal gait or during thenegotiation of obstacles. For example, FIG. 13 shows a raw signal fromthe fNIRS during trial 1 (obstacles) with no misstep detected. The lightline represents de-oxy hemoglobin and the dark line represents oxyhemoglobin. The time series is 40 seconds long. Note the sinusoidalrhythm reflects the pattern of walking and corresponds to heart ratemeasure. Optionally, the fNIRS signal is used to provide informationregarding, for example, heart rate and/or gait variability.

In addition, a correlation between changes in acceleration signal andchanges in physiological measures was assessed. FIG. 14 combines all 3measures and provides an indication that gait challenges can bereflected in physiological measures even when the event is short induration and the gait is executed on a treadmill. As shown in FIG. 14,information from all 3 physiological sensors is combined. The top signalis the acceleration. The ellipse reflects the time the system detectedthe misstep. The middle signal reflects the heart rate extracted fromthe NeXus and the signal box shows the oxy-hemoglobin signal from thefNIRS. Note the changes in all 3 signals during the event. After theevent there was an increase in both heart rate and blood flow to thefrontal lobe with a delay of, for example, 3-5 seconds.

Quantification

In an exemplary embodiment of the invention, fall risk score is acomposite measure based on two or more of the number of events detectedduring the test, gait parameters reflecting abnormal patterns (e.g., CV,PCI, symmetry) associated with fall risk, the response to the VRprovocations, number of errors on obstacle crossing, the cost ofenvironmental features (determined as the difference in stride timebetween trial 3−trial 4) and/or the cost of cognitive load onperformance (the difference in stride time between trial 5−trial 4) (seetable 2, FIG. 7). Using a weighted analysis, a score was provided on a 4point Likert scale. The fall risk score as determined using the systemfor each participant is presented below.

Subject 1

MW is an 83 year old woman with a history of 2 falls in the past year(one of which resulted in an injury to her wrist). She reports that shefeels unstable and has difficulty in crowded places to the point shetries to avoid going out. A total of 3 events were detected by thesystem during all of trials, 2 were validated by the researcherssuggesting a relatively low risk of falls. In some patients a targetnumber of falls or near-falls is set, for example, 3, 10, 50, 100, orother numbers and/or a misstep rate, for example, 1 in 10,000 steps, 1in 25,000 steps, 1 in 100,000 steps, 1 in 200,000 steps or smaller,intermediate or larger frequencies. The number of steps and/orchallenges may be adjusted to achieve such a desired rate and/orstatistical significance thereof. The gait events mainly occurred duringthe difficult trial and while environmental challenges were added. MWwalks with a very low clearance gait and often her gait appears asshuffling. 67% of the errors made on obstacle crossing were secondary tolow clearance which increases the risk for falls. Table 4 (FIG. 14)summarizes the results of her tests.

Subject 2

EB is a 67 year old man with no history of falls. EB served as ourcontrol subject. He is retired and has sustained a mild MI a year ago.EB is physically fit and walks 4 km everyday. He is cognitively intactbut reports forgetfulness on occasion. A total of 5 events were recordedby the system. Two of those were deemed by the researcher as changes inthe gait pattern that are not of a corrective nature. The mostdifficulty EB had was in the cognitive trial, where 2 events wererecorded and where most of his mistakes occurred. In the VF task, thesubject was able to recall only 5 words within 4 minutes. Thereforealthough he came into the study as a control subject, EB actually has anon-zero, mild risk of falls (mainly due to in attention) and couldbenefit from DT training within the VR consisting of walking whilenavigating in a VR environment rich with stimulus and attentiondemanding situations. Optionally, the therapist provides specificstrategies to follow or they are presented on the VR system. Optionallyor alternatively, the patient is allowed to formulate his ownstrategies, optionally with the system generating a signal if thestrategies are less desirable (e.g., walking speed below a thresholdvalue, shuffle steps). Table 5 (FIG. 15) summarizes the results of histests.

Subject 3

EB is a 68 year old woman with a history of 2 falls in the past year. Atotal of 7 events were recorded during the trials. Most of the eventswere due to inability to cross the obstacles (specifically the hurdles)as EB demonstrated a highly variable stepping pattern. In addition, theadded provocations decreased her ability to negotiate the obstacles.EB's gait was found to be asymmetrical and highly variable adding to herfall risk score. The specific findings of asymmetry may help prescribe atreatment for her to increase symmetry, improve control on the moreeffected limb and hence improve performance. Table 6 (FIG. 16)Summarizes the results of her tests.

Subject 4

AB is a 69 year old male with a history of 4 falls in the past year (2injurious). As per self report, his falls occur because of tripping overthings. The system detected 16 events during the trials. Most of theevents were due to inability to negotiate obstacles and 2 of the eventswere deemed as missteps. However AB also demonstrated difficulty in thecognitive trial with a high DT cost, and high variability of gaittherefore this patient may likely benefit from an interventionconsisting of walking while navigating in a VR environment rich withstimulus and attention demanding situations. All these measures combinedto produce a high risk of falls score. Table 7 (FIG. 17) Summarizes theresults of his tests.

Results of Therapeutic Application

Further experimentation tested the idea of treatment based on diagnosisand/or controlled level of challenges. Without being limited to aspecific hypothesis, it is possible that that motor learning principlesand/or bio-feedback can modify the locomotion strategies employed bysubjects who are prone to falling so that they will now be able toavert/reduce/recover from and/or otherwise assist approaching and/orongoing fall/misstep episodes. Possibly, the central nervous system(CNS) will be trained to modify the gait pattern in situations thattypically cause falling and/or near falls and/or in general mobility.

In an exemplary embodiment of the invention, a system is designed to beable to diagnose and quantify the risk of falls but also to providetreatment that is personalized to the person's needs using the VRsystem. The system uses a multi-modal treadmill training programaugmented by VR that addresses both motor and cognitive aspects of fallrisk and promotes motor learning critical for tasks that are key to safeambulation. A pilot study was conducted in which five elderly women(67.1±6.5 years) with a history of falls trained for 18 sessions (3 perweek×6 weeks), using the system described here. This training regimenwas chosen as it was based on motor learning principles to maximizeperformance, motor learning and plasticity. Training was set at 3 timesa week to allow for intensive treatment and consolidation of implicitinformation. Each session lasted approximately 1 hour including restbreaks, with actual walking time of approximately 50 minutes (beginningwith 20 minutes in the first session and adding 2 minutes to eachsession). Training duration was set at 6 weeks to provide an opportunityfor learning to take place and maximize retention. Other parameters maybe used as well, for example, longer or shorter sessions, longer orshorter durations and/or changes in training intensity over time.Training progression was individualized to meet the needs of theparticipant. The virtual environment (VE) consisted of, for example, oneor more obstacles, different pathways, narrow corridors and/ordistracters, which may require modulations of step amplitude in one, twoor three planes (e.g., height and width) coordinated with walkingbehavior. The speed, orientation, size, frequency of appearance and/orshape of the targets may be manipulated according to individual needsfollowing a standardized protocol. Environmental features (e.g.,visibility, settings and/or distractions) may be adjusted to increasetraining complexity. The VE imposed a cognitive load requiring attentionand response selection and/or processing of rich visual stimuliinvolving several perceptual processes. In the experiment, the systemprovided visual and auditory feedback of successful or unsuccessful taskperformance to enhance motor learning. In an exemplary embodiment of theinvention, the system is adaptable in that training parameters wereadjusted to the clinical needs of the individual participant. Eachtraining session lasted about 45 minutes and started with 5 minutes of“warm up” (only walking on the treadmill). After each warm-up phase, theVR simulation was introduced. The duration of continuous walking beforerest breaks (typically three to five minutes initially) and the totalwalking time were also increased throughout the sessions. Feedback wasgiven to the participant in the form of knowledge of results as ameasure of scoring on the obstacle avoidance tasks and knowledge ofperformance in the form of auditory and visual feedback if the subjectcontacted a (virtual) obstacle. The feedback was intended to enhancemotor learning and enable the modification of locomotion strategies tobe able to avert falls.

After training, gait speed over-ground significantly improved duringusual walking. More importantly, gait speed and stride time as well asvariability improved during walking under dual tasking and whilenegotiating over-ground obstacles. Dual task cost and over-groundobstacle clearance also improved. The subjects were followed for 6months post intervention and the frequency of falls was recorded duringthis period using fall calendars. In the follow up assessment, subjectsreported that their function at home improved as well as theirconfidence in walking. In addition there was a decrease of 73% in thefrequency of falls in the 6 months post-training as compared to 6 monthspre-training suggesting that the VR intervention may be effective forolder adults with a history of falls, may improve physical performance,improve gait during complex challenging conditions, decrease the risk offalls and may reduce falls.

General

It is expected that during the life of a patent maturing from thisapplication many relevant display technologies will be developed and thescopes of the terms display and virtual reality are intended to includeall such new technologies a priori.

As used herein the term “about” refers to ±10%.

The terms “comprises”, “comprising”, “includes”, “including”, “having”and their conjugates mean “including but not limited to”.

The term “consisting of” means “including and limited to”.

The term “consisting essentially of” means that the composition, methodor structure may include additional ingredients, steps and/or parts, butonly if the additional ingredients, steps and/or parts do not materiallyalter the basic and novel characteristics of the claimed composition,method or structure.

As used herein, the singular form “a”, “an” and “the” include pluralreferences unless the context clearly dictates otherwise. For example,the term “a compound” or “at least one compound” may include a pluralityof compounds, including mixtures thereof.

Throughout this application, various embodiments of this invention maybe presented in a range format. It should be understood that thedescription in range format is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 3, 4, 5, and 6. This appliesregardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to includeany cited numeral (fractional or integral) within the indicated range.The phrases “ranging/ranges between” a first indicate number and asecond indicate number and “ranging/ranges from” a first indicate number“to” a second indicate number are used herein interchangeably and aremeant to include the first and second indicated numbers and all thefractional and integral numerals therebetween.

As used herein the term “method” refers to manners, means, techniquesand procedures for accomplishing a given task including, but not limitedto, those manners, means, techniques and procedures either known to, orreadily developed from known manners, means, techniques and proceduresby practitioners of the chemical, pharmacological, biological,biochemical and medical arts.

As used herein, the term “treating” includes abrogating, substantiallyinhibiting, slowing or reversing the progression of a condition,substantially ameliorating clinical or aesthetical symptoms of acondition or substantially preventing the appearance of clinical oraesthetical symptoms of a condition.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable subcombination or as suitable in any other describedembodiment of the invention. Certain features described in the contextof various embodiments are not to be considered essential features ofthose embodiments, unless the embodiment is inoperative without thoseelements.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

It is the intent of the applicant(s) that all publications, patents andpatent applications referred to in this specification are to beincorporated in their entirety by reference into the specification, asif each individual publication, patent or patent application wasspecifically and individually noted when referenced that it is to beincorporated herein by reference. In addition, citation oridentification of any reference in this application shall not beconstrued as an admission that such reference is available as prior artto the present invention. To the extent that section headings are used,they should not be construed as necessarily limiting. In addition, anypriority document(s) of this application is/are hereby incorporatedherein by reference in its/their entirety.

What is claimed is:
 1. A method of training a subject, comprising:providing the subject with a VR (virtual reality) environment thatpresents the subject with at least one task to be performed duringlocomotion, said at least one task delivered by VR on a display, saidtask being one of a motor task, a cognitive task, a fall trigger, andany combination thereof, said at least one task including at least onetrigger which potentially at least one of induces a loss of balance andpresents at least one of at least one vertical obstacle and at least onehorizontal obstacle during the locomotion; measuring at least one of aphysiological parameter and a movement parameter of the subject duringperformance of the at least one task; processing said measuredparameter; and providing the subject with real-time feedback, based onsaid processed measured parameter, wherein said feedback includes atleast one marking indicating a gait irregularity during the locomotion,said at least one marking providing the subject with a reference as tohow to at least one of improve his balance and avoid the obstacle duringlocomotion.
 2. The method according to claim 1, wherein said presentingat least one task includes independently varying a cognitive load, agait complexity, and a potential fall trigger associated with said atleast one task.
 3. The method according to claim 1, wherein said task isone of: a cognitive task selected from decision making, using memory,planning, response selection, response inhibition, perception, dividedattention, and sustained attention; and a motor task selected fromfunctional reach, center of mass displacement, stepping, single limbsupport, and clearance of a foot over a horizontal or vertical obstacle.4. The method according to claim 1, further including allowing thesubject to at least one of correct and regulate his gait in response tosaid feedback.
 5. A method according to claim 1, wherein said presentingcomprises presenting dual motor and cognitive tasks.
 6. A methodaccording to claim 1, wherein said presenting comprises personalizingthe presentation to at least one of a physical feature of the subject,an activity of the subject, a weakness of the subject, a functionalability of the subject, a performance of the subject, a clinical historyof the subject.
 7. The method according to claim 1, further includingreceiving at least one input from the subject related to his locomotion.8. The method of claim 7, wherein said input is received by voicetransmission, pressing on buttons, and contacting a touch screen.
 9. Amethod according to claim 1, wherein said processing includesidentifying one or more parameters of a situation and/or a trigger whichinduces falls or near falls in the subject, wherein said trainingincludes setting up a training program responsive to the identifying.10. The method according to claim 1, wherein said feedback includes atleast one of biofeedback, auditory feedback, and visual feedback. 11.The method according to claim 1, where said feedback is in a form of oneof KP (knowledge of performance) and KR (knowledge of results).
 12. Themethod according to claim 1, wherein said providing feedback includesproviding a virtual avatar indicating a pattern of movement of thesubject during said locomotion.
 13. The method of claim 1, wherein saidtraining includes a regimen that automatically progresses in difficultylevel according to the feedback provided to the subject.
 14. The methodof claim 1, wherein said training includes a regimen having a pluralityof difficulty levels according to cognitive and motor requirements inperforming the tasks.
 15. The method of claim 14, wherein said regimenincludes five difficulty levels, as follows: a. Level A—a simulationwith obstacles having a first level of difficulty, both horizontal andvertical, with at least one of a cognitive load, a perceptual load, anda motor load; b. Level B—a simulation as in Level A, additionallyincluding obstacles having a second level of difficulty, said secondlevel of difficulty being higher than said first level of difficulty,and the addition of narrow pathways; c. Level C—a simulation as in LevelB, additionally including passages selected from doors, bridges, andtunnels; d. Level D—a simulation as in Level C, additionally includingauditory and visual distractions; and e. Level E—a simulation as inLevel D, additionally including combined dual task activities, obstacleshaving a third level of difficulty wherein said third level ofdifficulty is higher than said second level of difficulty, additionallyincluding narrow passages.
 16. The method of claim 1, wherein saidtraining is used in conjunction with teaching of alternative strategiesto reduce a fall risk, said alternative strategies being at least one ofcognitive, motor, and mechanical.
 17. The method according to claim 1,wherein the subject is a healthy subject and wherein said training isutilized to improve performance of said at least one task duringlocomotion of the healthy subject.
 18. The method of claim 1, whereinthe subject has at least one of a cognitive impairment, a motorimpairment, and a fall risk, and wherein said training is for thepurpose of at least one of changing the subject's susceptibility to theimpairment, improving gait, improving dual tasking and/or cognitiveability, and lowering a risk of falls.
 19. The method of claim 18,wherein said at least one task is selected according to the subject'scognitive impairment, motor impairment, and fall risk.
 20. The method ofclaim 18, wherein the subject has a cognitive impairment of one or moreof executive function, attention, planning, and visual spatialprocessing.
 21. The method of claim 18, wherein said training includesproviding a training regimen having at least one of a target number offalls or near-falls and a target misstep rate.
 22. The method of claim21, wherein a number of tasks in said training regimen is adjusted toachieve at least one of said target number of falls or near-falls andsaid target misstep rate.
 23. The method according to claim 1, furtherincluding providing a training regimen personalized for the subject,said personalization including the following training parameters:training duration, frequency of training sessions, duration of eachtraining session, and changes in these parameters over time.
 24. Themethod according to claim 1, wherein said VR environment includes atleast one obstacle, pathway, narrow corridor, and distraction whichcause the subject to modify his locomotion.
 25. The method of claim 1,wherein said VR environment includes at least one target having at leastone feature that is selected according to a subject protocol.
 26. Themethod of claim 25, wherein said at least one feature is selected fromspeed, orientation, size, frequency of appearance, and shape of saidtarget.
 27. The method of claim 1, wherein said VR environment includesat least one feature that may be adjusted to increase trainingcomplexity.
 28. The method of claim 27, wherein said at least onefeature is selected from visibility, setting, and distractions.
 29. Themethod of claim 1, wherein said VR environment is similar to that foundin everyday life of the subject.
 30. The method of claim 1, wherein theVR environment includes a treadmill on which the subject performs the atleast one task during locomotion.
 31. A method according to claim 1,wherein said physiological parameter is based on change of activity infrontal lobes of the subject.
 32. A method according to claim 31,wherein said change in activity is detected using EEG.
 33. A methodaccording to claim 31, wherein: an increase in blood flow to a frontallobe indicates a gait disorder due to lack of or over activity in thefrontal lobe; and wherein a decrease in blood flow to a frontal lobeindicates a gait disorder due to frontal lobe dysfunction.
 34. A methodaccording to claim 1, wherein said one or more physiological parametersincludes an anticipatory postural adjustment.